alternative computing technologies
DESCRIPTION
Alternative Computing Technologies. CS 8803 ACT Spring 2014 Hadi Esmaeilzadeh [email protected] Georgia Institute of Technology. Hadi Esmaeilzadeh From Khoy , Iran. PhD in CSE, University of Washington Doug Burger and Luis Ceze. 2013 William Chan Memorial Dissertation Award. - PowerPoint PPT PresentationTRANSCRIPT
Alternative Computing Technologies
CS 8803 ACTSpring 2014
Hadi [email protected] Georgia Institute of Technology
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Hadi EsmaeilzadehFrom Khoy, Iran
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PhD in CSE, University of WashingtonDoug Burger and Luis Ceze
2013 William Chan Memorial Dissertation Award
MSc in CS, The University of Texas at AustinMSc and BSc in ECE, University of Tehran
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Research: ACT LabAlternative Computing Technologies
General-purpose approximate computing Bridging neuromorphic and von Neumann
models of computing Analog computing System design for online machine learning System design for perpetual devices
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Agenda
1. Who is Hadi
2. Course organization3. Why alternative computing technologies
1. How we became and industry of new possibilities2. Why we might become an industry of replacement
4. Possible alternative computing technologies5. Quiz # 1
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Objective
Explore cutting-edge research on new and alternative paradigms of computing
Empower you with higher order critical thinking
Improve your technical writing and speaking Innovate in alternative computing
technologies
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Format
Seminar course– Reading papers– Critiquing and discussing the papers– Brainstorming about new ideas– Developing new technologies
Mostly your presentations– I will only lecture three times
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Grading rubric
Component FractionClass Presentation 30%Class Participation 10%Critiques 25%Final Project 35%
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Class presentation
Objective: Communicate and analyze ideas
4 points: Clearly presenting the key ideas 1 points: Clear, well-organized slides 5 points: Stimulating interesting discussion 1 point bonus
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Class participation
You have to say somethinginteresting!
By 9pm the night before, two comments/questions – Your new ideas– Critical questions about methodologies and conclusions– Why will the paper be cite– What you learned– Main insights from the papers
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Critiques
Objective: developing high-order critical thinking
Summary (quarter a page) Strengths (1-3 sentences) Weaknesses (1-3 sentences) Analysis I (1 paragraph) Analysis II (1 paragraph)
Please read the “The task of the referee by Alan Jay Smith”
Reading material for writing critiquesThe task of the referee
Allen Jay SmithStyle: The Basics of Clarity and Grace
Joseph M. Williams
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Final project
Groups of two Options
– Implementing a new idea– Extending an existing paper– Re-implement a paper– Survey at least ten papers
Evaluation– Implementation– Writing– Oral presentation
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Prerequisites
Understand a subset of– VLSI Circuits– Computer architecture– Programming Languages– Machine learning
Do– Programming
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Agenda
1. Who Hadi is2. Course organization
3. Why alternative computing technologies
1. How we became and industry of new possibilities2. Why we might become and industry of replacement
4. Possible alternative computing technologies5. Quiz # 1
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What has made computing pervasive? What is the backbone
of computing industry?
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Programmability Networking
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What makes computers programmable?
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von Neumann architectureGeneral-purpose processors
Components – Memory (RAM)– Central processing unit (CPU)
• Control unit• Arithmetic logic unit (ALU)
– Input/output system Memory stores program and data Program instructions execute sequentially
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Programmability versus Efficiency
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Programmability versus Efficiency
Efficiency
Prog
ram
mab
ility
General-Purpose Processors
FPGAsASICs
GPUs
SIMD Units
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What is the difference between the computing industry and the paper towel
industry?
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Industry of new possibilities
Industry of replacement
1971 2013
?
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Can we continue being an industry of new possibilities?
Personalizedhealthcare
Virtualreality
Real-timetranslators
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Agenda
1. Who Hadi is2. Course organization3. Why alternative computing technologies
1. How we became and industry of new possibilities
2. Why we might become and industry of replacement
4. Possible alternative computing technologies5. Quiz # 1
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Moore’s LawOr, how we became an industry of new possibilities
Double the number of transistors Build higher performance
general-purpose processors– Make the transistors available to masses– Increase performance (1.8×↑)– Lower the cost of computing (1.8×↓)
Every 2 Years
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What is the catch?Powering the transistors without melting the chip
1970 1975 1980 1985 1990 1995 2000 2005 2010 20150
1
100
10,000
1,000,000
100,000,000
10,000,000,000
0.5
130
2300
2200000000Chip Transistor CountChip Power
Moore’s Law
W
W
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Dennard scaling: Doubling the transistors; scale their power down
Dimensions
Voltage
DopingConcentrations
×0.7
Area 0.5×↓
Power 0.5×↓
Frequency 1.4×↑
Capacitance 0.7×↓
Transistor: 2D Voltage-Controlled Switch
Power = Capacitance × Frequency × Voltage2
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Dennard scaling broke: Double the transistors; still scale their power down
Dimensions
Voltage
DopingConcentrations
×0.7
Area 0.5×↓
Power 0.5×↓
Frequency 1.4×↑
Capacitance 0.7×↓
Transistor: 2D Voltage-Controlled Switch
Power = Capacitance × Frequency × Voltage2
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Dark siliconIf you cannot power them, why bother making them?
Area 0.5×↓Power 0.5×↓
Fraction of transistors that need to bepowered off at all times
due to power constraints
Dark Silicon
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Looking backEvolution of processors
1971 2003
Single-core Era
2004
2013
Multicore Era
Dennard scalingbroke
740 KHz
3.4 GHz 3.5 GHz
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Are multicores a long-term solution or just a stopgap?
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Agenda
1. Who Hadi is2. Course organization3. Why alternative computing technologies
1. How we became and industry of new possibilities
2. Why we might become an industry of replacement
4. Possible alternative computing technologies5. Quiz # 1
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Modeling future multicoresQuantify the severity of the problem
Predict the performance of best-case multicores– From 45 nm to 8 nm– Parallel benchmarks– Fixed power and area budget
Transistor Scaling Model
Single-CoreScaling Model
MulticoreScaling Model
Esmaeilzadeh, Belem, St. Amant, Sankaralingam, Burger, “Dark Silicon and the End of Multicore Scaling,” ISCA 2011
Transistor scaling modelFrom 45 nm to 8 nm
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Area
Power
Speed
[ITRS, 2010]
OptimisticScaling Model
32× ↓
8.3× ↓
3.9× ↑
[VLSI-DAT, 2010]
Conservative Scaling Model
32× ↓
4.5× ↓
1.3× ↑
[Dennard, 1974]
HistoricalScaling
32× ↓
32× ↓
5.7× ↑
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Single-core model (45 nm)
0 5 10 15 20 25 30 35 400
5
10
15
20
25
30Intel NehalemAMD ShanghaiIntel CoreIntel AtomPareto Frontier (45 nm)
Core Performance (SPECmark)
Core
Pow
er (W
atts)
Power-Performance and Area-PerformancePareto Optimal Frontiers
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Single-core scaling model
0 5 10 15 20 25 30 35 400
5
10
15
20
25
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Core Performance (SPECmark)
Core
Pow
er (W
atts)
Single-core Scaling Model:Single-core Model × Transistor Scaling Model
From 45 nm to 8 nm
Transistor Speed Scaling Factor
Transistor Power Scaling Factor
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Exhaustive search of multicore design space(Examine 800 design points for every technology node)
Multicore scaling model
Single Core Search Space(Scaled Area and Power Pareto Frontiers)
From 45 nm to 8 nm
Application Characteristics(% Parallel, % Memory Accesses)
Constraints(Area and Power Budget)
Microarchitectural Features(Cache and Memory Latencies, CPI,
Memory Bandwidth)
Multicore Topology(Symmetric, Asymmetric, Dynamic, Composable)
Multicore Organization: CPU-Like, GPU-Like
(# of HW Threads, Cache Sizes)
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Multicore model (Amdahl’s Law)
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Dark silicon
40%
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Evaluation Setup
Applications:– 12 PARSEC Parallel Benchmarks
Baseline:– The best multicore design available at 45 nm
Constraints:– Driven from the best multicore design at 45 nm
• Fixed Power Budget: 125 W• Fixed Area Budget: 111 mm2
42Dark
Sili
con 45 nm 32 nm 22 nm 16 nm 11 nm 8 nm
45 nm 32 nm 22 nm 16 nm 11 nm 8 nm0
4
8
12
16
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Historical Trend
Optimistic Transistor Scaling (Projection)
Conservative Transistor Scaling (Projection)
Perf
orm
ance
Impr
ovem
ent /
45
nm
10 years
1% 17% 36% 40% 51%
18×
3.7×
2013
7.9×
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Industry of replacement?
Multicores are likely to be a stopgap– Not likely to continue the historical trends– Do not overcome the transistor scaling trends– The performance gap is significantly large
Radical departures from conventional approaches are necessary– Extract more performance and efficiency from silicon
while preserving programmability– Explore other sources of computing
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Agenda
1. Who Hadi is2. Course organization3. Why alternative computing technologies
1. How we became and industry of new possibilities2. Why we might become and industry of replacement
4. Possible alternative computing technologies
5. Quiz # 1
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Alternative computing technologies
Approximate Computing
Perpetual Computing
Analog Computing
Stochastic Computing Human-basedComputing
Biological Computing
Neuromorphic Computing
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Approximate computingEmbracing error
Relax the abstraction of near-perfect accuracy in general-purpose computing
Allow errors to happen in the computation– Run faster– Run more efficiently
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New landscape of computingPersonalized and targeted computing
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Classes ofapproximate applications Programs with analog inputs– Sensors, scene reconstruction
Programs with analog outputs– Multimedia
Programs with multiple possible answers– Web search, machine learning
Convergent programs– Gradient descent, big data analytics
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Adding a third dimensionEmbracing Error
Erro
r
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A fertile ground for innovationEr
ror
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Approximate computing techniques
Same Model• Sampling– Loop perforation (MIT)
• Compression– Sage (Michigan)
• Early termination– Green (MSR)
• Replacement– Green (MSR)
• Lower voltage– Truffle (Rice, UW)
From Model to Model• von Neumann to Neural
– NPUs (UW, GaTech)
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Agenda
1. Who Hadi is2. Course organization3. Why alternative computing technologies
1. How we became and industry of new possibilities2. Why we might become an industry of replacement
4. Possible alternative computing technologies
5. Quiz # 1
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