scalable many-core memory systems lecture 3, topic 1: dram ... · " jamie liu, ben jaiyen,...

Scalable Many-Core Memory Systems Lecture 3, Topic 1: DRAM Basics and

DRAM Scaling

Prof. Onur Mutlu http://www.ece.cmu.edu/~omutlu

[email protected] HiPEAC ACACES Summer School 2013

July 17, 2013

New DRAM Architectures

n  RAIDR: Reducing Refresh Impact n  TL-DRAM: Reducing DRAM Latency n  SALP: Reducing Bank Conflict Impact n  RowClone: Fast Bulk Data Copy and Initialization

2

Subarray-Level Parallelism: Reducing Bank Conflict Impact

Yoongu Kim, Vivek Seshadri, Donghyuk Lee, Jamie Liu, and Onur Mutlu, "A Case for Exploiting Subarray-Level Parallelism (SALP) in DRAM"

Proceedings of the 39th International Symposium on Computer Architecture (ISCA), Portland, OR, June 2012. Slides (pptx)

The Memory Bank Conflict Problem

n  Two requests to the same bank are serviced serially n  Problem: Costly in terms of performance and power n  Goal: We would like to reduce bank conflicts without

increasing the number of banks (at low cost)

n  Idea: Exploit the internal sub-array structure of a DRAM bank to parallelize bank conflicts q  By reducing global sharing of hardware between sub-arrays

n  Kim, Seshadri, Lee, Liu, Mutlu, “A Case for Exploiting Subarray-Level Parallelism in DRAM,” ISCA 2012.

4

5

!me Wr Rd

Wr Rd !me

Bank

!me

Bank

Bank

•  Two Banks

•  One Bank

1. Serializa!on

Wr Wr Rd Rd Wr 2 Wr 2 Rd Rd Wr 2 Wr 2 Rd Rd 3 3 3

2. Write Penalty 3. Thrashing Row-‐Buffer

Served in parallel

Wasted

The Problem with Memory Bank Conflicts

Goal •  Goal: Mi!gate the detrimental effects of bank conflicts in a cost-‐effec!ve manner

•  Naïve solu3on: Add more banks –  Very expensive

•  Cost-‐effec3ve solu3on: Approximate the benefits of more banks without adding more banks

6

A DRAM bank is divided into subarrays Key Observa3on #1

7

Row

Row-‐Buffer

Row Row Row

32k rows

Logical Bank

A single row-‐buffer cannot drive all rows

Global Row-‐Buf

Physical Bank

Local Row-‐Buf

Local Row-‐Buf Subarray1

Subarray64

Many local row-‐buffers, one at each subarray

Key Observa3on #2 Each subarray is mostly independent…

–  except occasionally sharing global structures

8

Global Row-‐Buf

Glob

al Decod

er

Bank

Local Row-‐Buf

Local Row-‐Buf Subarray1

Subarray64

·∙·∙·∙

Key Idea: Reduce Sharing of Globals

9

Global Row-‐Buf

Glob

al Decod

er

Bank

Local Row-‐Buf

Local Row-‐Buf

·∙·∙·∙

1. Parallel access to subarrays

2. USlize mulSple local row-‐buffers

Overview of Our Mechanism

10

·∙·∙·∙

Req Req

Global Row-‐Buf

Local Row-‐Buf

Req

Local Row-‐Buf

Req 1. Parallelize

2. U!lize mul!ple local row-‐buffers

Subarray64

Subarray1 To same bank... but diff. subarrays

Challenges: Global Structures 1. Global Address Latch

2. Global Bitlines

11

Local row-‐buffer

Local row-‐buffer Global row-‐buffer

Challenge #1. Global Address Latch

12 ·∙·∙·∙ addr

VDD

addr

Glob

al Decod

er

VDD

Latch

Latch

Latch PRECHAR

GED

ACTIVATED

ACTIVATED

Local row-‐buffer

Local row-‐buffer Global row-‐buffer

Solu3on #1. Subarray Address Latch

13

·∙·∙·∙

VDD

Glob

al Decod

er

VDD

Latch

Latch

Latch ACTIVATE

D

ACTIVATED

Global latch à local latches


•  Problem: Only one raised wordline •  SoluSon: Subarray Address Latch

2. Global Bitlines

14

Challenge #2. Global Bitlines

15

Local row-‐buffer

Local row-‐buffer

Switch

Switch

READ

Global bitlines

Global row-‐buffer

Collision

Wire

Solu3on #2. Designated-‐Bit Latch

16

Global bitlines

Global row-‐buffer

Local row-‐buffer

Local row-‐buffer

Switch

Switch

READ READ

D D

D D

SelecEvely connect local to global


•  Problem: Only one raised wordline •  SoluSon: Subarray Address Latch

2. Global Bitlines •  Problem: Collision during access •  SoluSon: Designated-‐Bit Latch

17 MASA (Mul3tude of Ac3vated Subarrays)

•  Baseline (Subarray-‐Oblivious)

• MASA

MASA: Advantages

18

!me Wr 2 Wr 2 Rd Rd 3 3 3

1. Serializa!on

2. Write Penalty 3. Thrashing

!me Wr

Wr

Rd

Rd

Saved

MASA: Overhead •  DRAM Die Size: 0.15% increase

–  Subarray Address Latches –  Designated-‐Bit Latches & Wire

•  DRAM Sta3c Energy: Small increase –  0.56mW for each acSvated subarray –  But saves dynamic energy

•  Controller: Small addiSonal storage –  Keep track of subarray status (< 256B) –  Keep track of new Sming constraints

19

Cheaper Mechanisms

20

D

D

Latches

1. SerializaS

on

2. W

r-‐Pe

nalty

3. Thrashing

MASA

SALP-‐2

SALP-‐1

System Configura3on •  System Configura3on

–  CPU: 5.3GHz, 128 ROB, 8 MSHR –  LLC: 512kB per-‐core slice

•  Memory Configura3on –  DDR3-‐1066 –  (default) 1 channel, 1 rank, 8 banks, 8 subarrays-‐per-‐bank –  (sensi!vity) 1-‐8 chans, 1-‐8 ranks, 8-‐64 banks, 1-‐128 subarrays

•  Mapping & Row-‐Policy –  (default) Line-‐interleaved & Closed-‐row –  (sensi!vity) Row-‐interleaved & Open-‐row

•  DRAM Controller Configura3on –  64-‐/64-‐entry read/write queues per-‐channel –  FR-‐FCFS, batch scheduling for writes

21

SALP: Single-‐core Results

22

0% 10% 20% 30% 40% 50% 60% 70% 80%

IPC Im

provem

ent MASA "Ideal"

17%

20%

MASA achieves most of the benefit of having more banks (“Ideal”)

SALP: Single-‐Core Results

23

0%

10%

20%

30%

IPC Increase

SALP-‐1 SALP-‐2 MASA "Ideal"

SALP-‐1, SALP-‐2, MASA improve performance at low cost

20% 17% 13% 7%

DRAM Die Area < 0.15% 0.15% 36.3%

Subarray-‐Level Parallelism: Results

24

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Normalized

Dy

namic Ene

rgy

Baseline MASA

0%

20%

40%

60%

80%

100%

Row-‐Buff

er Hit-‐Ra

te

Baseline MASA

MASA increases energy-‐efficiency

-‐19%

+13%

New DRAM Architectures

n  RAIDR: Reducing Refresh Impact n  TL-DRAM: Reducing DRAM Latency n  SALP: Reducing Bank Conflict Impact n  RowClone: Fast Bulk Data Copy and Initialization

25

RowClone: Fast Bulk Data Copy and Initialization

Vivek Seshadri, Yoongu Kim, Chris Fallin, Donghyuk Lee, Rachata Ausavarungnirun, Gennady Pekhimenko, Yixin Luo, Onur Mutlu, Phillip B. Gibbons, Michael A. Kozuch, Todd C. Mowry,

"RowClone: Fast and Efficient In-DRAM Copy and Initialization of Bulk Data" CMU Computer Science Technical Report, CMU-CS-13-108, Carnegie Mellon University, April 2013.

Today’s Memory: Bulk Data Copy

Memory

MC L3 L2 L1 CPU

1) High latency

2) High bandwidth uSlizaSon

3) Cache polluSon

4) Unwanted data movement

27

Future: RowClone (In-‐Memory Copy)

Memory

MC L3 L2 L1 CPU

1) Low latency

2) Low bandwidth uSlizaSon

3) No cache polluSon

4) No unwanted data movement

28 Seshadri et al., “RowClone: Fast and Efficient In-DRAM Copy and Initialization of Bulk Data,” CMU Tech Report 2013.

DRAM operation (load one byte)

Row Buffer (4 Kbits)

Memory Bus

Data pins (8 bits)

DRAM array

4 Kbits

1. Activate row

2. Transfer row

3. Transfer byte onto bus

RowClone: in-DRAM Row Copy (and Initialization)

Row Buffer (4 Kbits)

Memory Bus

Data pins (8 bits)

DRAM array

4 Kbits

1. Activate row A

2. Transfer row

3. Activate row B

4. Transfer row

RowClone: Key Idea •  DRAM banks contain

1.  MuSple rows of DRAM cells – row = 8KB 2.  A row buffer shared by the DRAM rows

•  Large scale copy 1.  Copy data from source row to row buffer 2.  Copy data from row buffer to desSnaSon row Can be accomplished by two consecu3ve ACTIVATEs (if source and des3na3on rows are in the same subarray)

31

RowClone: Intra-‐subarray Copy

0 1 0 0 1 1 0 0 0 1 1 0

1 1 0 1 0 1 1 1 0 0 1 1

AcSvate (src) DeacSvate (our proposal) AcSvate (dst)

0 1 0 0 1 1 0 0 0 1 1 0

? ? ? ? ? ? ? ? ? ? ? ? 0 1 0 0 1 1 0 0 0 1 1 0

Sense Amplifiers (row buffer)

src

dst

32

RowClone: Inter-‐bank Copy

I/O Bus Transfer

(our proposal)

src

dst

Read Write

33

RowClone: Inter-‐subarray Copy

I/O Bus 1. Transfer (src to temp)

src

dst

temp

2. Transfer (temp to dst) 34

Fast Row IniSalizaSon

0 0 0 0 0 0 0 0 0 0 0 0

Fix a row at Zero (0.5% loss in capacity)

35

RowClone: Latency and Energy Savings

0

0.2

0.4

0.6

0.8

1

1.2

Latency Energy

Normalized

Savings

Baseline Intra-‐Subarray Inter-‐Bank Inter-‐Subarray

11.5x 74x

36 Seshadri et al., “RowClone: Fast and Efficient In-DRAM Copy and Initialization of Bulk Data,” CMU Tech Report 2013.

37

Intra-‐subarray

Intra-‐subarray

RowClone: Latency and Energy Savings

RowClone: Overall Performance

38

Agenda for Topic 1 (DRAM Scaling)

n  What Will You Learn in This Mini-Lecture Series n  Main Memory Basics (with a Focus on DRAM) n  Major Trends Affecting Main Memory n  DRAM Scaling Problem and Solution Directions n  Solution Direction 1: System-DRAM Co-Design n  Ongoing Research n  Summary

39

Sampling of Ongoing Research

n  Online retention time profiling q  Preliminary work in ISCA 2013 q  Jamie Liu, Ben Jaiyen, Yoongu Kim, Chris Wilkerson, and Onur Mutlu,

"An Experimental Study of Data Retention Behavior in Modern DRAM Devices: Implications for Retention Time Profiling Mechanisms" Proceedings of the 40th International Symposium on Computer Architecture (ISCA), Tel-Aviv, Israel, June 2013. Slides (pptx) Slides (pdf)

n  More computation in memory and controllers

n  Refresh/demand parallelization

40

Agenda for Topic 1 (DRAM Scaling)

n  What Will You Learn in This Mini-Lecture Series n  Main Memory Basics (with a Focus on DRAM) n  Major Trends Affecting Main Memory n  DRAM Scaling Problem and Solution Directions n  Solution Direction 1: System-DRAM Co-Design n  Ongoing Research n  Summary

41

Summary n  Major problems with DRAM scaling and design: high refresh

rate, high latency, low parallelism, bulk data movement

n  Four new DRAM designs q  RAIDR: Reduces refresh impact q  TL-DRAM: Reduces DRAM latency at low cost q  SALP: Improves DRAM parallelism q  RowClone: Reduces energy and performance impact of bulk data copy

n  All four designs q  Improve both performance and energy consumption q  Are low cost (low DRAM area overhead) q  Enable new degrees of freedom to software & controllers

n  Rethinking DRAM interface and design essential for scaling q  Co-design DRAM with the rest of the system

42

Further Reading: Data Retention and Power

n  Characterization of Commodity DRAM Chips q  Jamie Liu, Ben Jaiyen, Yoongu Kim, Chris Wilkerson, and Onur Mutlu,

"An Experimental Study of Data Retention Behavior in Modern DRAM Devices: Implications for Retention Time Profiling Mechanisms" Proceedings of the 40th International Symposium on Computer Architecture (ISCA), Tel-Aviv, Israel, June 2013. Slides (pptx) Slides (pdf)

n  Voltage and Frequency Scaling in DRAM q  Howard David, Chris Fallin, Eugene Gorbatov, Ulf R. Hanebutte, and Onur

Mutlu, "Memory Power Management via Dynamic Voltage/Frequency Scaling" Proceedings of the 8th International Conference on Autonomic Computing (ICAC), Karlsruhe, Germany, June 2011. Slides (pptx) (pdf)

43

Scalable Many-Core Memory Systems Topic 1: DRAM Basics and

DRAM Scaling

Prof. Onur Mutlu http://www.ece.cmu.edu/~omutlu

[email protected] HiPEAC ACACES Summer School 2013

July 15-19, 2013

Additional Material (Not Covered in Lecture)

45

Three Papers

n  Howard David, Chris Fallin, Eugene Gorbatov, Ulf R. Hanebutte, and Onur Mutlu, "Memory Power Management via Dynamic Voltage/Frequency Scaling" Proceedings of the 8th International Conference on Autonomic Computing (ICAC), Karlsruhe, Germany, June 2011. Slides (pptx) (pdf)

n  Jamie Liu, Ben Jaiyen, Yoongu Kim, Chris Wilkerson, and Onur Mutlu, "An Experimental Study of Data Retention Behavior in Modern DRAM Devices: Implications for Retention Time Profiling Mechanisms" Proceedings of the 40th International Symposium on Computer Architecture (ISCA), Tel-Aviv, Israel, June 2013. Slides (pptx) Slides (pdf)

n  Yu Cai, Gulay Yalcin, Onur Mutlu, Erich F. Haratsch, Adrian Cristal, Osman Unsal, and Ken Mai, "Error Analysis and Retention-Aware Error Management for NAND Flash Memory" Intel Technology Journal (ITJ) Special Issue on Memory Resiliency, Vol. 17, No. 1, May 2013.

46

Memory Power Management via Dynamic Voltage/Frequency Scaling

Howard David (Intel) Eugene Gorbatov (Intel) Ulf R. Hanebutte (Intel)

Chris Fallin (CMU) Onur Mutlu (CMU)

Memory Power is Significant n  Power consumption is a primary concern in modern servers n  Many works: CPU, whole-system or cluster-level approach n  But memory power is largely unaddressed n  Our server system*: memory is 19% of system power (avg)

q  Some work notes up to 40% of total system power

n  Goal: Can we reduce this figure?

48

0 100 200 300 400

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Power (W

)

System Power Memory Power

*Dual 4-core Intel Xeon®, 48GB DDR3 (12 DIMMs), SPEC CPU2006, all cores active. Measured AC power, analytically modeled memory power.

Existing Solution: Memory Sleep States? n  Most memory energy-efficiency work uses sleep states

q  Shut down DRAM devices when no memory requests active

n  But, even low-memory-bandwidth workloads keep memory awake q  Idle periods between requests diminish in multicore workloads q  CPU-bound workloads/phases rarely completely cache-resident

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0% 2% 4% 6% 8%

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t in Sleep

States

Sleep State Residency

Memory Bandwidth Varies Widely n  Workload memory bandwidth requirements vary widely

n  Memory system is provisioned for peak capacity

à often underutilized

50

0

2

4

6

8

Band

width/cha

nnel (G

B/s)

Memory Bandwidth for SPEC CPU2006

Memory Power can be Scaled Down n  DDR can operate at multiple frequencies à reduce power

q  Lower frequency directly reduces switching power q  Lower frequency allows for lower voltage q  Comparable to CPU DVFS

n  Frequency scaling increases latency à reduce performance q  Memory storage array is asynchronous q  But, bus transfer depends on frequency q  When bus bandwidth is bottleneck, performance suffers

51

CPU Voltage/Freq.

System Power

Memory Freq.

System Power

↓ 15% ↓ 9.9% ↓ 40% ↓ 7.6%

Observations So Far n  Memory power is a significant portion of total power

q  19% (avg) in our system, up to 40% noted in other works

n  Sleep state residency is low in many workloads q  Multicore workloads reduce idle periods q  CPU-bound applications send requests frequently enough

to keep memory devices awake

n  Memory bandwidth demand is very low in some workloads

n  Memory power is reduced by frequency scaling q  And voltage scaling can give further reductions

52

DVFS for Memory n  Key Idea: observe memory bandwidth utilization, then

adjust memory frequency/voltage, to reduce power with minimal performance loss

à Dynamic Voltage/Frequency Scaling (DVFS) for memory

n  Goal in this work: q  Implement DVFS in the memory system, by: q  Developing a simple control algorithm to exploit opportunity

for reduced memory frequency/voltage by observing behavior q  Evaluating the proposed algorithm on a real system

53

Outline n  Motivation

n  Background and Characterization q  DRAM Operation q  DRAM Power q  Frequency and Voltage Scaling

n  Performance Effects of Frequency Scaling

n  Frequency Control Algorithm

n  Evaluation and Conclusions

54






55

DRAM Operation n  Main memory consists of DIMMs of DRAM devices n  Each DIMM is attached to a memory bus (channel) n  Multiple DIMMs can connect to one channel

56

Memory Bus (64 bits)

/8 /8 /8 /8 /8 /8 /8 /8

to Memory Controller

Inside a DRAM Device

57

Bank 0

Sense Amps Column Decoder

Row

Dec

oder

ODT

Reci

ever

s D

river

s

Regi

ster

s

Writ

e FI

FO

Banks •  Independent arrays •  Asynchronous:

independent of memory bus speed

I/O Circuitry •  Runs at bus speed •  Clock sync/distribution •  Bus drivers and receivers •  Buffering/queueing

On-Die Termination •  Required by bus electrical characteristics

for reliable operation •  Resistive element that dissipates power

when bus is active

Effect of Frequency Scaling on Power n  Reduced memory bus frequency: n  Does not affect bank power:

q  Constant energy per operation q  Depends only on utilized memory bandwidth

n  Decreases I/O power: q  Dynamic power in bus interface and clock circuitry

reduces due to less frequent switching n  Increases termination power:

q  Same data takes longer to transfer q  Hence, bus utilization increases

n  Tradeoff between I/O and termination results in a net power reduction at lower frequencies

58

Effects of Voltage Scaling on Power n  Voltage scaling further reduces power because all parts of

memory devices will draw less current (at less voltage) n  Voltage reduction is possible because stable operation

requires lower voltage at lower frequency:

59

1 1.1 1.2 1.3 1.4 1.5 1.6

1333MHz 1066MHz 800MHz

DIMM Voltage (V

)

Minimum Stable Voltage for 8 DIMMs in a Real System

Vdd for Power Model






60

How Much Memory Bandwidth is Needed?

61

0 1 2 3 4 5 6 7

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width/cha

nnel (G

B/s)

Memory Bandwidth for SPEC CPU2006

Performance Impact of Static Frequency Scaling

62

n  Performance impact is proportional to bandwidth demand n  Many workloads tolerate lower frequency with minimal

performance drop

0 10 20 30 40 50 60 70 80

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Performan

ce Drop (%

)

Performance Loss, Sta3c Frequency Scaling

1333-‐>800

1333-‐>1066

0

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Performan

ce D

rop (%

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Performance Loss, Sta3c Frequency Scaling

1333-‐>800 1333-‐>1066

:: :: :: :: :: :: :: :: :: :: : : : : : :






63

Memory Latency Under Load n  At low load, most time is in array access and bus transfer

à small constant offset between bus-frequency latency curves

n  As load increases, queueing delay begins to dominate à bus frequency significantly affects latency

64

60 90

120 150 180

0 2000 4000 6000 8000

Latency (ns)

U3lized Channel Bandwidth (MB/s)

Memory Latency as a Func3on of Bandwidth and Mem Frequency


Control Algorithm: Demand-Based Switching

After each epoch of length Tepoch: Measure per-channel bandwidth BW if BW < T800 : switch to 800MHz else if BW < T1066 : switch to 1066MHz else : switch to 1333MHz

65

60 90

120 150 180

0 2000 4000 6000 8000

Latency (ns)

U3lized Channel Bandwidth (MB/s)

Memory Latency as a Func3on of Bandwidth and Mem Frequency


T1066 T800

Implementing V/F Switching n  Halt Memory Operations

q  Pause requests q  Put DRAM in Self-Refresh q  Stop the DIMM clock

n  Transition Voltage/Frequency q  Begin voltage ramp q  Relock memory controller PLL at new frequency q  Restart DIMM clock q  Wait for DIMM PLLs to relock

n  Begin Memory Operations q  Take DRAM out of Self-Refresh q  Resume requests

66

C Memory frequency already adjustable statically

C Voltage regulators for CPU DVFS can work for memory DVFS

C Full transition takes ~20µs






67

Evaluation Methodology n  Real-system evaluation

q  Dual 4-core Intel Xeon®, 3 memory channels/socket

q  48 GB of DDR3 (12 DIMMs, 4GB dual-rank, 1333MHz)

n  Emulating memory frequency for performance q  Altered memory controller timing registers (tRC, tB2BCAS) q  Gives performance equivalent to slower memory frequencies

n  Modeling power reduction q  Measure baseline system (AC power meter, 1s samples) q  Compute reductions with an analytical model (see paper)

68

Evaluation Methodology

n  Workloads q  SPEC CPU2006: CPU-intensive workloads q  All cores run a copy of the benchmark

n  Parameters q  Tepoch = 10ms q  Two variants of algorithm with different switching thresholds: q  BW(0.5, 1): T800 = 0.5GB/s, T1066 = 1GB/s q  BW(0.5, 2): T800 = 0.5GB/s, T1066 = 2GB/s

à More aggressive frequency/voltage scaling

69

Performance Impact of Memory DVFS n  Minimal performance degradation: 0.2% (avg), 1.7% (max) n  Experimental error ~1%

70

-‐1

0

1

2

3

4

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AVG

Performan

ce Degrada

3on (%

)

BW(0.5,1) BW(0.5,2)

Memory Frequency Distribution n  Frequency distribution shifts toward higher memory frequencies with more memory-intensive benchmarks

71

0%

20%

40%

60%

80%

100%

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1333 1066 800

Memory Power Reduction n  Memory power reduces by 10.4% (avg), 20.5% (max)

72

0

5

10

15

20

25

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AVG Mem

ory Po

wer Red

uc3o

n (%

)

BW(0.5,1) BW(0.5,2)

System Power Reduction

73

0 0.5 1

1.5 2

2.5 3

3.5 4

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AVG Sy

stem

Pow

er Red

uc3o

n (%

)

BW(0.5,1) BW(0.5,2)

n  As a result, system power reduces by 1.9% (avg), 3.5% (max)

n  System energy reduces by 2.4% (avg), 5.1% (max)

System Energy Reduction

74

-‐1

0

1

2

3

4

5

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AVG

System

Ene

rgy Re

duc3on

(%)

BW(0.5,1) BW(0.5,2)

Related Work n  MemScale [Deng11], concurrent work (ASPLOS 2011)

q  Also proposes Memory DVFS q  Application performance impact model to decide voltage and

frequency: requires specific modeling for a given system; our bandwidth-based approach avoids this complexity

q  Simulation-based evaluation; our work is a real-system proof of concept

n  Memory Sleep States (Creating opportunity with data placement

[Lebeck00,Pandey06], OS scheduling [Delaluz02], VM subsystem [Huang05]; Making better decisions with better models [Hur08,Fan01])

n  Power Limiting/Shifting (RAPL [David10] uses memory throttling for thermal limits; CPU throttling for memory traffic [Lin07,08]; Power shifting across system [Felter05])

75

Conclusions n  Memory power is a significant component of system power

q  19% average in our evaluation system, 40% in other work

n  Workloads often keep memory active but underutilized q  Channel bandwidth demands are highly variable q  Use of memory sleep states is often limited

n  Scaling memory frequency/voltage can reduce memory power with minimal system performance impact q  10.4% average memory power reduction q  Yields 2.4% average system energy reduction

n  Greater reductions are possible with wider frequency/voltage range and better control algorithms

76

Memory Power Management via Dynamic Voltage/Frequency Scaling

Howard David (Intel) Eugene Gorbatov (Intel) Ulf R. Hanebutte (Intel)

Chris Fallin (CMU) Onur Mutlu (CMU)

1 Carnegie Mellon University 2 Intel Corporation

Jamie Liu1 Ben Jaiyen1 Yoongu Kim1 Chris Wilkerson2 Onur Mutlu1

An Experimental Study of Data Retention Behavior

in Modern DRAM Devices

Implications for Retention Time Profiling Mechanisms

Summary (I) n  DRAM requires periodic refresh to avoid data loss

q  Refresh wastes energy, reduces performance, limits DRAM density scaling n  Many past works observed that different DRAM cells can retain data for

different times without being refreshed; proposed reducing refresh rate for strong DRAM cells q  Problem: These techniques require an accurate profile of the retention time of

all DRAM cells

n  Our goal: To analyze the retention time behavior of DRAM cells in modern DRAM devices to aid the collection of accurate profile information

n  Our experiments: We characterize 248 modern commodity DDR3 DRAM chips from 5 manufacturers using an FPGA based testing platform

n  Two Key Issues: 1. Data Pattern Dependence: A cell’s retention time is heavily dependent on data values stored in itself and nearby cells, which cannot easily be controlled. 2. Variable Retention Time: Retention time of some cells change unpredictably from high to low at large timescales.

Summary (II) n  Key findings on Data Pattern Dependence

q  There is no observed single data pattern that elicits the lowest retention times for a DRAM device à very hard to find this pattern

q  DPD varies between devices due to variation in DRAM array circuit design between manufacturers

q  DPD of retention time gets worse as DRAM scales to smaller feature sizes

n  Key findings on Variable Retention Time q  VRT is common in modern DRAM cells that are weak q  The timescale at which VRT occurs is very large (e.g., a cell can stay

in high retention time state for a day or longer) à finding minimum retention time can take very long

n  Future work on retention time profiling must address these issues

80

Talk Agenda n  DRAM Refresh: Background and Motivation n  Challenges and Our Goal n  DRAM Characterization Methodology n  Foundational Results

q  Temperature Dependence q  Retention Time Distribution

n  Data Pattern Dependence: Analysis and Implications n  Variable Retention Time: Analysis and Implications n  Conclusions

81

A DRAM Cell

n  A DRAM cell consists of a capacitor and an access transistor n  It stores data in terms of charge in the capacitor n  A DRAM chip consists of (10s of 1000s of) rows of such cells

wordline

bitli

ne

bitli

ne

bitli

ne

bitli

ne

bitli

ne

(row enable)

DRAM Refresh

n  DRAM capacitor charge leaks over time

n  Each DRAM row is periodically refreshed to restore charge q  Activate each row every N ms q  Typical N = 64 ms

n  Downsides of refresh -- Energy consumption: Each refresh consumes energy

-- Performance degradation: DRAM rank/bank unavailable while refreshed

-- QoS/predictability impact: (Long) pause times during refresh -- Refresh rate limits DRAM capacity scaling

83

Refresh Overhead: Performance

84

8%

46%

Liu et al., “RAIDR: Retention-Aware Intelligent DRAM Refresh,” ISCA 2012.

Refresh Overhead: Energy

85

15%

47%


Previous Work on Reducing Refreshes n  Observed significant variation in data retention times of

DRAM cells (due to manufacturing process variation) q  Retention time: maximum time a cell can go without being

refreshed while maintaining its stored data

n  Proposed methods to take advantage of widely varying retention times among DRAM rows q  Reduce refresh rate for rows that can retain data for longer

than 64 ms, e.g., [Liu+ ISCA 2012]

q  Disable rows that have low retention times, e.g., [Venkatesan+ HPCA 2006]

n  Showed large benefits in energy and performance

86

1. Profiling: Profile the retention time of all DRAM rows 2. Binning: Store rows into bins by retention time à use Bloom Filters for efficient and scalable storage 3. Refreshing: Memory controller refreshes rows in different bins at different rates à probe Bloom Filters to determine refresh rate of a row

An Example: RAIDR [Liu+, ISCA 2012]

87

1.25KB storage in controller for 32GB DRAM memory

Can reduce refreshes by ~75% à reduces energy consumption and improves performance

Problem: Requires accurate profiling of DRAM row retention times


Motivation n  Past works require accurate and reliable measurement of

retention time of each DRAM row q  To maintain data integrity while reducing refreshes

n  Assumption: worst-case retention time of each row can be determined and stays the same at a given temperature q  Some works propose writing all 1’s and 0’s to a row, and

measuring the time before data corruption

n  Question: q  Can we reliably and accurately determine retention times of all

DRAM rows?

88




89

Two Challenges to Retention Time Profiling n  Data Pattern Dependence (DPD) of retention time

n  Variable Retention Time (VRT) phenomenon

90

Two Challenges to Retention Time Profiling n  Challenge 1: Data Pattern Dependence (DPD)

q  Retention time of a DRAM cell depends on its value and the values of cells nearby it

q  When a row is activated, all bitlines are perturbed simultaneously

91

n  Electrical noise on the bitline affects reliable sensing of a DRAM cell n  The magnitude of this noise is affected by values of nearby cells via

q  Bitline-bitline coupling à electrical coupling between adjacent bitlines q  Bitline-wordline coupling à electrical coupling between each bitline and

the activated wordline

n  Retention time of a cell depends on data patterns stored in nearby cells

à need to find the worst data pattern to find worst-case retention time

Data Pattern Dependence

92

Two Challenges to Retention Time Profiling n  Challenge 2: Variable Retention Time (VRT)

q  Retention time of a DRAM cell changes randomly over time n  a cell alternates between multiple retention time states

q  Leakage current of a cell changes sporadically due to a charge trap in the gate oxide of the DRAM cell access transistor

q  When the trap becomes occupied, charge leaks more readily from the transistor’s drain, leading to a short retention time n  Called Trap-Assisted Gate-Induced Drain Leakage

q  This process appears to be a random process [Kim+ IEEE TED’11]

q  Worst-case retention time depends on a random process à need to find the worst case despite this

93

Our Goal n  Analyze the retention time behavior of DRAM cells in

modern commodity DRAM devices q  to aid the collection of accurate profile information

n  Provide a comprehensive empirical investigation of two key challenges to retention time profiling q  Data Pattern Dependence (DPD) q  Variable Retention Time (VRT)

94




95

DRAM Testing Platform and Method n  Test platform: Developed a DDR3 DRAM testing platform

using the Xilinx ML605 FPGA development board q  Temperature controlled

n  Tested DRAM chips: 248 commodity DRAM chips from five manufacturers (A,B,C,D,E)

n  Seven families based on equal capacity per device: q  A 1Gb, A 2Gb q  B 2Gb q  C 2Gb q  D 1Gb, D 2Gb q  E 2Gb

96

Experiment Design n  Each module tested for multiple rounds of tests.

n  Each test searches for the set of cells with a retention time less than a threshold value for a particular data pattern

n  High-level structure of a test: q  Write data pattern to rows in a DRAM bank q  Prevent refresh for a period of time tWAIT, leave DRAM idle q  Read stored data pattern, compare to written pattern and

record corrupt cells as those with retention time < tWAIT

n  Test details and important issues to pay attention to are discussed in paper

97

Experiment Structure

98

Round 1

Data Pattern XtWAIT = 1.5s

Data Pattern YtWAIT = 1.5s

Data Pattern ZtWAIT = 1.5s










Round 2

Test Round Tests both the data pattern and its complement

Experiment Parameters n  Most tests conducted at 45 degrees Celsius

n  No cells observed to have a retention time less than 1.5 second at 45oC

n  Tested tWAIT in increments of 128ms from 1.5 to 6.1 seconds

99

Tested Data Patterns n  All 0s/1s: Value 0/1 is written to all bits

q  Previous work suggested this is sufficient

n  Checkerboard: Consecutive bits alternate between 0 and 1 q  Coupling noise increases with voltage difference between the

neighboring bitlines à May induce worst case data pattern (if adjacent bits mapped to adjacent cells)

n  Walk: Attempts to ensure a single cell storing 1 is surrounded by cells storing 0 q  This may lead to even worse coupling noise and retention time due to

coupling between nearby bitlines [Li+ IEEE TCSI 2011] q  Walk pattern is permuted in each round to exercise different cells

n  Random: Randomly generated data is written to each row q  A new set of random data is generated for each round

100

Fixed patterns




101

Temperature Stability

102

0 1 2 3 4 5 6 7Time (Hours)

45

50

55

60

65

70

75

Tem

pera

ture

(C)

50C 55C 60C 65C 70C

Tested chips at five different stable temperatures

Dependence of Retention Time on Temperature

103

50 55 60 65 70Temperature (C)

0.2

0.4

0.6

0.8

1.0

Nor

mal

ized

Ret

entio

n Ti

me

Exponential fit, peakExponential fit, tail

0.000

0.025

0.050

0.075

0.100

0.125

0.150

0.175

0.200

Frac

tion

of W

eak

Cells

Fraction of cells that exhibited retention

time failure at any tWAIT

for any data pattern at 50oC

Normalized retention times of the same cells

at 55oC

Normalized retention times of the same cells

At 70oC

Best-fit exponential curves for retention time change

with temperature

Dependence of Retention Time on Temperature

104

50 55 60 65 70Temperature (C)

0.2

0.4

0.6

0.8

1.0

Nor

mal

ized

Ret

entio

n Ti

me

Exponential fit, peakExponential fit, tail

0.000

0.025

0.050

0.075

0.100

0.125

0.150

0.175

0.200

Frac

tion

of W

eak

Cells

Rela3onship between reten3on 3me and temperature is consistently bounded (predictable) within a device

Every 10oC temperature increase à 46.5% reduc3on in reten3on 3me in the worst case

Retention Time Distribution

105

0 1 2 3 4 5 6 7Retention Time (s)

0.00000

0.00001

0.00002

0.00003

0.00004

0.00005

0.00006

0.00007

0.00008Fr

actio

n of

Cel

ls w

ith R

eten

tion

Tim

e<

X-Ax

is Va

lue

C 2Gb

D 1Gb

D 2Gb

A 2Gb

A 1Gb

E 2Gb

B 2Gb

Minimum tested reten3on 3me ~1.5s at 45C à ~126ms at 85C Very few cells exhibit the lowest reten3on 3mes Shape of the curve consistent with previous works Newer device families have more weak cells than older ones Likely a result of technology scaling

OLDER

NEWER

OLDER

NEWER




106

Some Terminology n  Failure population of cells with Retention Time X: The set of

all cells that exhibit retention failure in any test with any data pattern at that retention time (tWAIT)

n  Retention Failure Coverage of a Data Pattern DP: Fraction of cells with retention time X that exhibit retention failure with that particular data pattern DP

n  If retention times are not dependent on data pattern stored in cells, we would expect q  Coverage of any data pattern to be 100% q  In other words, if one data pattern causes a retention failure,

any other data pattern also would

107

Recall the Tested Data Patterns n  All 0s/1s: Value 0/1 is written to all bits

n  Checkerboard: Consecutive bits alternate between 0 and 1

n  Walk: Attempts to ensure a single cell storing 1 is surrounded by cells storing 0

n  Random: Randomly generated data is written to each row

108

Fixed patterns

Retention Failure Coverage of Data Patterns

109

0 2 4 6 8 10 12 14 16Number of Rounds

0.0

0.2

0.4

0.6

0.8

1.0

Cove

rage

All 0s/1s

Checkerboard

Random

Walk

All 0s/1s Checkerboard Walk Random

A 2Gb chip family 6.1s retention time

Walk is the most effec3ve data pamern for this device Coverage of fixed pamerns is low: ~30% for All 0s/1s

No data pamern achieves 100% coverage

Different data pamerns have widely different coverage: Data pamern dependence exists and is severe


110

0 2 4 6 8 10 12 14 16Number of Rounds

0.0

0.2

0.4

0.6

0.8

1.0

Cove

rage

All 0s/1sCheckerboard

Random

Walk


B 2Gb chip family 6.1s retention time

Random is the most effec3ve data pamern for this device No data pamern achieves 100% coverage


111

0 2 4 6 8 10 12 14 16Number of Rounds

0.0

0.2

0.4

0.6

0.8

1.0

Cove

rage

All 0s/1s

Checkerboard

Random

Walk


Random is the most effec3ve data pamern for this device No data pamern achieves 100% coverage

C 2Gb chip family 6.1s retention time

Data Pattern Dependence: Observations (I) n  A cell’s retention time is heavily influenced by data pattern

stored in other cells q  Pattern affects the coupling noise, which affects cell leakage

n  No tested data pattern exercises the worst case retention time for all cells (no pattern has 100% coverage) q  No pattern is able to induce the worst-case coupling noise for

every cell q  Problem: Underlying DRAM circuit organization is not known to

the memory controller à very hard to construct a pattern that exercises the worst-case cell leakage à Opaque mapping of addresses to physical DRAM geometry à Internal remapping of addresses within DRAM to tolerate faults à Second order coupling effects are very hard to determine

112

Data Pattern Dependence: Observations (II) n  Fixed, simple data patterns have low coverage

q  They do not exercise the worst-case coupling noise

n  The effectiveness of each data pattern varies significantly between DRAM devices (of the same or different vendors) q  Underlying DRAM circuit organization likely differs between

different devices à patterns leading to worst coupling are different in different devices

n  Technology scaling appears to increase the impact of data pattern dependence q  Scaling reduces the physical distance between circuit elements,

increasing the magnitude of coupling effects

113

Effect of Technology Scaling on DPD

114

0 2 4 6 8 10 12 14 16Number of Rounds

0.0

0.2

0.4

0.6

0.8

1.0

Cove

rage

All 0s/1s

Checkerboard

Walk

All 0s/1s Checkerboard Walk

0 2 4 6 8 10 12 14 16Number of Rounds

0.0

0.2

0.4

0.6

0.8

1.0

Cove

rage

All 0s/1s

Checkerboard

Random

Walk


A 1Gb chip family A 2Gb chip family

The lowest-‐coverage data pamern achieves much lower coverage for the smaller technology node

DPD: Implications on Profiling Mechanisms n  Any retention time profiling mechanism must handle data pattern

dependence of retention time n  Intuitive approach: Identify the data pattern that induces the

worst-case retention time for a particular cell or device

n  Problem 1: Very hard to know at the memory controller which bits actually interfere with each other due to q  Opaque mapping of addresses to physical DRAM geometry à

logically consecutive bits may not be physically consecutive q  Remapping of faulty bitlines/wordlines to redundant ones internally

within DRAM

n  Problem 2: Worst-case coupling noise is affected by non-obvious second order bitline coupling effects

115

DPD: Suggestions (for Future Work) n  A mechanism for identifying worst-case data pattern(s)

likely requires support from DRAM device q  DRAM manufacturers might be in a better position to do this q  But, the ability of the manufacturer to identify and expose the

entire retention time profile is limited due to VRT

n  An alternative approach: Use random data patterns to increase coverage as much as possible; handle incorrect retention time estimates with ECC q  Need to keep profiling time in check q  Need to keep ECC overhead in check

116




117

Variable Retention Time n  Retention time of a cell can vary over time

n  A cell can randomly switch between multiple leakage current states due to Trap-Assisted Gate-Induced Drain Leakage, which appears to be a random process

[Yaney+ IEDM 1987, Restle+ IEDM 1992]

118

An Example VRT Cell

119

0 2 4 6 8 10Time (Hours)

0

1

2

3

4

5

6

7Re

tent

ion

Tim

e (s

)

A cell from E 2Gb chip family

VRT: Questions and Methodology n  Key Questions

q  How prevalent is VRT in modern DRAM devices? q  What is the timescale of observation of the lowest retention

time state? q  What are the implications on retention time profiling?

n  Test Methodology q  Each device was tested for at least 1024 rounds over 24 hours q  Temperature fixed at 45oC q  Data pattern used is the most effective data pattern for each

device q  For each cell that fails at any retention time, we record the

minimum and the maximum retention time observed

120

Variable Retention Time

121

0 1 2 3 4 5 6 7Minimum Retention Time (s)

0

1

2

3

4

5

6

7M

axim

um R

eten

tion

Tim

e (s

)

6.0

5.4

4.8

4.2

3.6

3.0

2.4

1.8

1.2

0.6

0.0

log1

0(Fr

actio

n of

Cel

ls)

A 2Gb chip family

Min ret time = Max ret time Expected if no VRT

Most failing cells exhibit VRT

Many failing cells jump from very high retention time to very low


122


0

1

2

3

4

5

6

7M

axim

um R

eten

tion

Tim

e (s

)

6.0

5.4

4.8

4.2

3.6

3.0

2.4

1.8

1.2

0.6

0.0

log1

0(Fr

actio

n of

Cel

ls)

B 2Gb chip family


123


0

1

2

3

4

5

6

7M

axim

um R

eten

tion

Tim

e (s

)

6.0

5.4

4.8

4.2

3.6

3.0

2.4

1.8

1.2

0.6

0.0

log1

0(Fr

actio

n of

Cel

ls)

C 2Gb chip family

VRT: Observations So Far n  VRT is common among weak cells (i.e., those cells that

experience low retention times)

n  VRT can result in significant retention time changes q  Difference between minimum and maximum retention times of

a cell can be more than 4x, and may not be bounded q  Implication: Finding a retention time for a cell and using a

guardband to ensure minimum retention time is “covered” requires a large guardband or may not work

n  Retention time profiling mechanisms must identify lowest retention time in the presence of VRT q  Question: How long to profile a cell to find its lowest retention

time state? 124

Time Between Retention Time State Changes

n  How much time does a cell spend in a high retention state before switching to the minimum observed retention time state?

125

Time Spent in High Retention Time State

126

0 20000 40000 60000 80000Time Spent in High Retention Time State (s)

Rela

tive

Freq

uenc

y

A 2Gb chip family

~4 hours ~1 day

Time scale at which a cell switches to the low reten3on 3me state can be very long (~ 1 day or longer) Need to profile for a long 3me to

get to the minimum reten3on 3me state


127


Rela

tive

Freq

uenc

y

B 2Gb chip family


128


Rela

tive

Freq

uenc

y

C 2Gb chip family

VRT: Implications on Profiling Mechanisms n  Problem 1: There does not seem to be a way of

determining if a cell exhibits VRT without actually observing a cell exhibiting VRT q  VRT is a memoryless random process [Kim+ JJAP 2010]

n  Problem 2: VRT complicates retention time profiling by DRAM manufacturers q  Exposure to very high temperatures can induce VRT in cells that

were not previously susceptible à can happen during soldering of DRAM chips à manufacturer’s retention time profile may not be accurate

n  One option for future work: Use ECC to continuously profile DRAM online while aggressively reducing refresh rate q  Need to keep ECC overhead in check

129




130

Summary and Conclusions n  DRAM refresh is a critical challenge in scaling DRAM technology

efficiently to higher capacities and smaller feature sizes n  Understanding the retention time of modern DRAM devices can

enable old or new methods to reduce the impact of refresh q  Many mechanisms require accurate and reliable retention time profiles

n  We presented the first work that comprehensively examines data retention behavior in modern commodity DRAM devices q  Characterized 248 devices from five manufacturers

n  Key findings: Retention time of a cell significantly depends on data pattern stored in other cells (data pattern dependence) and changes over time via a random process (variable retention time) q  Discussed the underlying reasons and provided suggestions

n  Future research on retention time profiling should solve the challenges posed by the DPD and VRT phenomena

131

1 Carnegie Mellon University 2 Intel Corporation

Jamie Liu1 Ben Jaiyen1 Yoongu Kim1 Chris Wilkerson2 Onur Mutlu1

An Experimental Study of Data Retention Behavior

in Modern DRAM Devices

Implications for Retention Time Profiling Mechanisms

Flash Memory Scaling

Aside: Scaling Flash Memory [Cai+, ICCD’12]

134

n  NAND flash memory has low endurance: a flash cell dies after 3k P/E cycles vs. 50k desired à Major scaling challenge for flash memory

n  Flash error rate increases exponentially over flash lifetime n  Problem: Stronger error correction codes (ECC) are ineffective and

undesirable for improving flash lifetime due to q  diminishing returns on lifetime with increased correction strength q  prohibitively high power, area, latency overheads

n  Our Goal: Develop techniques to tolerate high error rates w/o strong ECC n  Observation: Retention errors are the dominant errors in MLC NAND flash

q  flash cell loses charge over time; retention errors increase as cell gets worn out n  Solution: Flash Correct-and-Refresh (FCR)

q  Periodically read, correct, and reprogram (in place) or remap each flash page before it accumulates more errors than can be corrected by simple ECC

q  Adapt “refresh” rate to the severity of retention errors (i.e., # of P/E cycles)

n  Results: FCR improves flash memory lifetime by 46X with no hardware changes and low energy overhead; outperforms strong ECCs

Readings in Flash Memory n  Yu Cai, Gulay Yalcin, Onur Mutlu, Erich F. Haratsch, Adrian Cristal, Osman Unsal, and Ken Mai,

"Error Analysis and Retention-Aware Error Management for NAND Flash Memory" Intel Technology Journal (ITJ) Special Issue on Memory Resiliency, Vol. 17, No. 1, May 2013.

n  Yu Cai, Erich F. Haratsch, Onur Mutlu, and Ken Mai, "Threshold Voltage Distribution in MLC NAND Flash Memory: Characterization, Analysis and Modeling" Proceedings of the Design, Automation, and Test in Europe Conference (DATE), Grenoble, France, March 2013. Slides (ppt)

n  Yu Cai, Gulay Yalcin, Onur Mutlu, Erich F. Haratsch, Adrian Cristal, Osman Unsal, and Ken Mai, "Flash Correct-and-Refresh: Retention-Aware Error Management for Increased Flash Memory Lifetime" Proceedings of the 30th IEEE International Conference on Computer Design (ICCD), Montreal, Quebec, Canada, September 2012. Slides (ppt) (pdf)

n  Yu Cai, Erich F. Haratsch, Onur Mutlu, and Ken Mai, "Error Patterns in MLC NAND Flash Memory: Measurement, Characterization, and Analysis" Proceedings of the Design, Automation, and Test in Europe Conference (DATE), Dresden, Germany, March 2012. Slides (ppt)

135

Evolution of NAND Flash Memory

n  Flash memory widening its range of applications q  Portable consumer devices, laptop PCs and enterprise servers

Seaung Suk Lee, “Emerging Challenges in NAND Flash Technology”, Flash Summit 2011 (Hynix)

CMOS scaling More bits per Cell

UBER: Uncorrectable bit error rate. Fraction of erroneous bits after error correction.

Decreasing Endurance with Flash Scaling

n  Endurance of flash memory decreasing with scaling and multi-level cells n  Error correction capability required to guarantee storage-class reliability

(UBER < 10-15) is increasing exponentially to reach less endurance

137

Ariel Maislos, “A New Era in Embedded Flash Memory”, Flash Summit 2011 (Anobit)

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

SLC 5x-nm MLC 3x-nm MLC 2x-nm MLC 3-bit-MLC

P/E

Cyc

le E

ndur

ance

100k

10k 5k 3k 1k

4-bit ECC

8-bit ECC

15-bit ECC

24-bit ECC

Error Correction Capability (per 1 kB of data)

Future NAND Flash Storage Architecture

Memory Signal

Processing

Error Correction

Raw Bit Error Rate

•  Hamming codes •  BCH codes •  Reed-Solomon codes •  LDPC codes •  Other Flash friendly codes

BER < 10-15

Need to understand NAND flash error patterns

•  Read voltage adjusting •  Data scrambler •  Data recovery •  Soft-information estimation

Noisy

Test System Infrastructure

Host USB PHY

USB Driver

Software Platform

USB PHYChip

Control Firmware

FPGA USB controller

NAND Controller

Signal Processing

Wear Leveling Address Mapping Garbage Collection

Algorithms

ECC (BCH, RS, LDPC)

Flash Memories

Host Computer USB Daughter Board Mother Board Flash Board

1.  Reset 2.  Erase block 3.  Program page 4.  Read page

NAND Flash Testing Platform

USB Jack

Virtex-II Pro (USB controller)

Virtex-V FPGA (NAND Controller)

HAPS-52 Mother Board

USB Daughter Board

NAND Daughter Board

3x-nm NAND Flash

NAND Flash Usage and Error Model

…

(Page0 - Page128) Program

Page Erase Block

Retention1 (t1 days)

Read Page

Retention j (tj days)

Read Page

P/E cycle 0

P/E cycle i

Start

…

P/E cycle n

…

End of life

Erase Errors Program Errors

Retention Errors Read Errors

Read Errors Retention Errors

Error Types and Testing Methodology n  Erase errors

q  Count the number of cells that fail to be erased to “11” state

n  Program interference errors q  Compare the data immediately after page programming and the data

after the whole block being programmed

n  Read errors q  Continuously read a given block and compare the data between

consecutive read sequences

n  Retention errors q  Compare the data read after an amount of time to data written

n  Characterize short term retention errors under room temperature n  Characterize long term retention errors by baking in the oven

under 125℃

retention errors

n  Raw bit error rate increases exponentially with P/E cycles n  Retention errors are dominant (>99% for 1-year ret. time) n  Retention errors increase with retention time requirement

Observations: Flash Error Analysis

143

P/E Cycles

Retention Error Mechanism LSB/MSB

n  Electron loss from the floating gate causes retention errors q  Cells with more programmed electrons suffer more from

retention errors q  Threshold voltage is more likely to shift by one window than by

multiple

11 10 01 00 Vth

REF1 REF2 REF3

Erased Fully programmed

Stress Induced Leakage Current (SILC)

Floating Gate

Retention Error Value Dependency

00 à01 01 à10

n  Cells with more programmed electrons tend to suffer more from retention noise (i.e. 00 and 01)

More Details on Flash Error Analysis

n  Yu Cai, Erich F. Haratsch, Onur Mutlu, and Ken Mai, "Error Patterns in MLC NAND Flash Memory: Measurement, Characterization, and Analysis" Proceedings of the Design, Automation, and Test in Europe Conference (DATE), Dresden, Germany, March 2012. Slides (ppt)

146

Threshold Voltage Distribution Shifts

As P/E cycles increase ... n Distribution shifts to the right n Distribution becomes wider

P1 State P2 State P3 State

More Detail

n  Yu Cai, Erich F. Haratsch, Onur Mutlu, and Ken Mai, "Threshold Voltage Distribution in MLC NAND Flash Memory: Characterization, Analysis and Modeling" Proceedings of the Design, Automation, and Test in Europe Conference (DATE), Grenoble, France, March 2013. Slides (ppt)

148

Flash Correct-and-Refresh

Retention-Aware Error Management for Increased Flash Memory Lifetime

Yu Cai1 Gulay Yalcin2 Onur Mutlu1 Erich F. Haratsch3 Adrian Cristal2 Osman S. Unsal2 Ken Mai1

1 Carnegie Mellon University 2 Barcelona Supercomputing Center 3 LSI Corporation

Executive Summary n  NAND flash memory has low endurance: a flash cell dies after 3k P/E

cycles vs. 50k desired à Major scaling challenge for flash memory n  Flash error rate increases exponentially over flash lifetime n  Problem: Stronger error correction codes (ECC) are ineffective and

undesirable for improving flash lifetime due to q  diminishing returns on lifetime with increased correction strength q  prohibitively high power, area, latency overheads

n  Our Goal: Develop techniques to tolerate high error rates w/o strong ECC n  Observation: Retention errors are the dominant errors in MLC NAND flash

q  flash cell loses charge over time; retention errors increase as cell gets worn out n  Solution: Flash Correct-and-Refresh (FCR)

q  Periodically read, correct, and reprogram (in place) or remap each flash page before it accumulates more errors than can be corrected by simple ECC

q  Adapt “refresh” rate to the severity of retention errors (i.e., # of P/E cycles)

n  Results: FCR improves flash memory lifetime by 46X with no hardware changes and low energy overhead; outperforms strong ECCs

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Outline n  Executive Summary n  The Problem: Limited Flash Memory Endurance/Lifetime n  Error and ECC Analysis for Flash Memory n  Flash Correct and Refresh Techniques (FCR) n  Evaluation n  Conclusions

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Problem: Limited Endurance of Flash Memory n  NAND flash has limited endurance

q  A cell can tolerate a small number of Program/Erase (P/E) cycles q  3x-nm flash with 2 bits/cell à 3K P/E cycles

n  Enterprise data storage requirements demand very high endurance q  >50K P/E cycles (10 full disk writes per day for 3-5 years)

n  Continued process scaling and more bits per cell will reduce flash endurance

n  One potential solution: stronger error correction codes (ECC) q  Stronger ECC not effective enough and inefficient

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UBER: Uncorrectable bit error rate. Fraction of erroneous bits after error correction.

Decreasing Endurance with Flash Scaling

n  Endurance of flash memory decreasing with scaling and multi-level cells n  Error correction capability required to guarantee storage-class reliability

(UBER < 10-15) is increasing exponentially to reach less endurance

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Ariel Maislos, “A New Era in Embedded Flash Memory”, Flash Summit 2011 (Anobit)

0

10,000

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SLC 5x-nm MLC 3x-nm MLC 2x-nm MLC 3-bit-MLC

P/E

Cyc

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ndur

ance

100k

10k 5k 3k 1k

4-bit ECC

8-bit ECC

15-bit ECC

24-bit ECC

Error Correction Capability (per 1 kB of data)

The Problem with Stronger Error Correction

n  Stronger ECC detects and corrects more raw bit errors à increases P/E cycles endured

n  Two shortcomings of stronger ECC: 1. High implementation complexity à Power and area overheads increase super-linearly, but

correction capability increases sub-linearly with ECC strength

2. Diminishing returns on flash lifetime improvement à Raw bit error rate increases exponentially with P/E cycles, but

correction capability increases sub-linearly with ECC strength

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Methodology: Error and ECC Analysis n  Characterized errors and error rates of 3x-nm MLC NAND

flash using an experimental FPGA-based flash platform q  Cai et al., “Error Patterns in MLC NAND Flash Memory:

Measurement, Characterization, and Analysis,” DATE 2012.

n  Quantified Raw Bit Error Rate (RBER) at a given P/E cycle q  Raw Bit Error Rate: Fraction of erroneous bits without any correction

n  Quantified error correction capability (and area and power consumption) of various BCH-code implementations q  Identified how much RBER each code can tolerate

à how many P/E cycles (flash lifetime) each code can sustain

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NAND Flash Error Types

n  Four types of errors [Cai+, DATE 2012]

n  Caused by common flash operations q  Read errors q  Erase errors q  Program (interference) errors

n  Caused by flash cell losing charge over time q  Retention errors

n  Whether an error happens depends on required retention time n  Especially problematic in MLC flash because voltage threshold

window to determine stored value is smaller

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retention errors

n  Raw bit error rate increases exponentially with P/E cycles n  Retention errors are dominant (>99% for 1-year ret. time) n  Retention errors increase with retention time requirement

Observations: Flash Error Analysis

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P/E Cycles

Methodology: Error and ECC Analysis n  Characterized errors and error rates of 3x-nm MLC NAND

flash using an experimental FPGA-based flash platform q  Cai et al., “Error Patterns in MLC NAND Flash Memory:

Measurement, Characterization, and Analysis,” DATE 2012.

n  Quantified Raw Bit Error Rate (RBER) at a given P/E cycle q  Raw Bit Error Rate: Fraction of erroneous bits without any correction

n  Quantified error correction capability (and area and power consumption) of various BCH-code implementations q  Identified how much RBER each code can tolerate

à how many P/E cycles (flash lifetime) each code can sustain

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ECC Strength Analysis n  Examined characteristics of various-strength BCH codes

with the following criteria q  Storage efficiency: >89% coding rate (user data/total storage) q  Reliability: <10-15 uncorrectable bit error rate q  Code length: segment of one flash page (e.g., 4kB)

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Code length (n)

Correctable Errors (t)

Acceptable Raw BER

Norm. Power

Norm. Area

512 7 1.0x10-4 (1x) 1 11024 12 4.0x10-4 (4x) 2 2.12048 22 1.0x10-3 (10x) 4.1 3.94096 40 1.7x10-3 (17x) 8.6 10.38192 74 2.2x10-3 (22x) 17.8 21.332768 259 2.6x10-3 (26x) 71 85

Error correcSon capability increases sub-‐linearly

Power and area overheads increase super-‐linearly

n  Lifetime improvement comparison of various BCH codes

Resulting Flash Lifetime with Strong ECC

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512b-BCH 1k-BCH 2k-BCH 4k-BCH 8k-BCH 32k-BCH

P/E

Cyc

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ndur

ance

4X Lifetime Improvement

71X Power Consumption 85X Area Consumption

Strong ECC is very inefficient at improving lifeSme

Our Goal

Develop new techniques to improve flash lifetime without relying on stronger ECC

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Flash Correct-and-Refresh (FCR) n  Key Observations:

q  Retention errors are the dominant source of errors in flash memory [Cai+ DATE 2012][Tanakamaru+ ISSCC 2011]

à limit flash lifetime as they increase over time q  Retention errors can be corrected by “refreshing” each flash

page periodically

n  Key Idea: q  Periodically read each flash page, q  Correct its errors using “weak” ECC, and q  Either remap it to a new physical page or reprogram it in-place, q  Before the page accumulates more errors than ECC-correctable q  Optimization: Adapt refresh rate to endured P/E cycles

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FCR Intuition

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Errors with No refresh

ProgramPage ×

After time T × × ×

After time 2T × × × × ×

After time 3T × × × × × × ×

×

× × ×

× × ×

× × ×

×

×

Errors with Periodic refresh

×

× Retention Error × Program Error

FCR: Two Key Questions

n  How to refresh? q  Remap a page to another one q  Reprogram a page (in-place) q  Hybrid of remap and reprogram

n  When to refresh? q  Fixed period q  Adapt the period to retention error severity

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Outline n  Executive Summary n  The Problem: Limited Flash Memory Endurance/Lifetime n  Error and ECC Analysis for Flash Memory n  Flash Correct and Refresh Techniques (FCR)

1. Remapping based FCR 2. Hybrid Reprogramming and Remapping based FCR 3. Adaptive-Rate FCR

n  Evaluation n  Conclusions

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Remapping Based FCR

n  Idea: Periodically remap each page to a different physical page (after correcting errors)

q  Also [Pan et al., HPCA 2012]

q  FTL already has support for changing logical à physical flash block/page mappings q  Deallocated block is erased by garbage collector

n  Problem: Causes additional erase operations à more wearout q  Bad for read-intensive workloads (few erases really needed) q  Lifetime degrades for such workloads (see paper)

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In-Place Reprogramming Based FCR

n  Idea: Periodically reprogram (in-place) each physical page (after correcting errors)

q  Flash programming techniques (ISPP) can correct retention errors in-place by recharging flash cells

n  Problem: Program errors accumulate on the same page à may not be correctable by ECC after some time

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Reprogram corrected data

n  Pro: No remapping needed à no additional erase operations n  Con: Increases the occurrence of program errors

In-Place Reprogramming of Flash Cells

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Retention errors are caused by cell voltage shifting to the left

ISPP moves cell voltage to the right; fixes retention errors

Floating Gate Voltage Distribution

for each Stored Value

Floating Gate

Program Errors in Flash Memory

n  When a cell is being programmed, voltage level of a neighboring cell changes (unintentionally) due to parasitic capacitance coupling

à can change the data value stored

n  Also called program interference error

n  Program interference causes neighboring cell voltage to shift to the right

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Problem with In-Place Reprogramming

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11 10 01 00 VT

REF1 REF2 REF3

Floating Gate

Additional Electrons Injected

… … 11 01 00 10 11 00 00 Original data to be programmed

… … 10 01 00 10 11 00 00 Program errors after initial programming

… … Retention errors after some time 10 10 00 11 11 01 01

… … Errors after in-place reprogramming 10 01 00 10 10 00 00

1. Read data 2. Correct errors 3. Reprogram back

Problem: Program errors can accumulate over time

Floating Gate Voltage Distribution

Hybrid Reprogramming/Remapping Based FCR

n  Idea: q  Monitor the count of right-shift errors (after error correction) q  If count < threshold, in-place reprogram the page q  Else, remap the page to a new page

n  Observation: q  Program errors much less frequent than retention errors à

Remapping happens only infrequently

n  Benefit: q  Hybrid FCR greatly reduces erase operations due to remapping

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Adaptive-Rate FCR

n  Observation: q  Retention error rate strongly depends on the P/E cycles a flash

page endured so far q  No need to refresh frequently (at all) early in flash lifetime

n  Idea: q  Adapt the refresh rate to the P/E cycles endured by each page q  Increase refresh rate gradually with increasing P/E cycles

n  Benefits: q  Reduces overhead of refresh operations q  Can use existing FTL mechanisms that keep track of P/E cycles

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Adaptive-Rate FCR (Example)

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Acceptable raw BER for 512b-BCH

3-year FCR

3-month FCR

3-week FCR

3-day FCR

P/E Cycles

Select refresh frequency such that error rate is below acceptable rate




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FCR: Other Considerations

n  Implementation cost q  No hardware changes q  FTL software/firmware needs modification

n  Response time impact q  FCR not as frequent as DRAM refresh; low impact

n  Adaptation to variations in retention error rate q  Adapt refresh rate based on, e.g., temperature [Liu+ ISCA 2012]

n  FCR requires power q  Enterprise storage systems typically powered on

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Evaluation Methodology n  Experimental flash platform to obtain error rates at

different P/E cycles [Cai+ DATE 2012]

n  Simulation framework to obtain P/E cycles of real workloads: DiskSim with SSD extensions

n  Simulated system: 256GB flash, 4 channels, 8 chips/channel, 8K blocks/chip, 128 pages/block, 8KB pages

n  Workloads q  File system applications, databases, web search q  Categories: Write-heavy, read-heavy, balanced

n  Evaluation metrics q  Lifetime (extrapolated) q  Energy overhead, P/E cycle overhead

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Extrapolated Lifetime

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Maximum full disk P/E Cycles for a Technique

Total full disk P/E Cycles for a Workload × # of Days of Given Application

Obtained from Experimental Platform Data

Obtained from Workload Simulation Real length (in time) of each workload trace

Normalized Flash Memory Lifetime

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Base (No-‐Refresh) Remapping-‐Based FCR Hybrid FCR AdapSve FCR

46x

Adap3ve-‐rate FCR provides the highest life3me Life3me of FCR much higher than life3me of stronger ECC

4x

Lifetime Evaluation Takeaways n  Significant average lifetime improvement over no refresh

q  Adaptive-rate FCR: 46X q  Hybrid reprogramming/remapping based FCR: 31X q  Remapping based FCR: 9X

n  FCR lifetime improvement larger than that of stronger ECC q  46X vs. 4X with 32-kbit ECC (over 512-bit ECC) q  FCR is less complex and less costly than stronger ECC

n  Lifetime on all workloads improves with Hybrid FCR q  Remapping based FCR can degrade lifetime on read-heavy WL q  Lifetime improvement highest in write-heavy workloads

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Energy Overhead

n  Adaptive-rate refresh: <1.8% energy increase until daily

refresh is triggered

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Remapping-based Refresh Hybrid Refresh

7.8%

5.5%

2.6% 1.8%

0.4% 0.3%

Refresh Interval

Overhead of Additional Erases

n  Additional erases happen due to remapping of pages

n  Low (2%-20%) for write intensive workloads n  High (up to 10X) for read-intensive workloads

n  Improved P/E cycle lifetime of all workloads largely outweighs the additional P/E cycles due to remapping

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More Results in the Paper

n  Detailed workload analysis

n  Effect of refresh rate

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Conclusion n  NAND flash memory lifetime is limited due to uncorrectable

errors, which increase over lifetime (P/E cycles)

n  Observation: Dominant source of errors in flash memory is retention errors à retention error rate limits lifetime

n  Flash Correct-and-Refresh (FCR) techniques reduce retention error rate to improve flash lifetime q  Periodically read, correct, and remap or reprogram each page

before it accumulates more errors than can be corrected q  Adapt refresh period to the severity of errors

n  FCR improves flash lifetime by 46X at no hardware cost q  More effective and efficient than stronger ECC q  Can enable better flash memory scaling

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Flash Correct-and-Refresh

Retention-Aware Error Management for Increased Flash Memory Lifetime

Yu Cai1 Gulay Yalcin2 Onur Mutlu1 Erich F. Haratsch3 Adrian Cristal2 Osman S. Unsal2 Ken Mai1

1 Carnegie Mellon University 2 Barcelona Supercomputing Center 3 LSI Corporation

scalable many-core memory systems lecture 3, topic 1: dram ... · " jamie liu, ben jaiyen,...

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