computer architecture lecture 26 past and future ralph grishman november 2015 nyu
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IC Scaling for Integration Current IC production: basic dimension = 14 nm chips with 5 billion transistors Planning for next generation at 7 nm will require new transistor geometries and probably new materials hard to predict beyond that fabrication becoming very expensive, affordable by only a few companies more cores on chip network on chip issues 11/30/15Computer Architecture lecture 243TRANSCRIPT
Computer ArchitectureLecture 26
Past and Future
Ralph GrishmanNovember 2015
NYU
Computer Architecture lecture 24 2
IC Scaling for Speed• Smaller transistors
faster transistors faster clock
• but also more heat
• power wall at about 4 GHz, can’t run faster
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Computer Architecture lecture 24 3
IC Scaling for Integration• Current IC production:
• basic dimension = 14 nm• chips with 5 billion transistors
• Planning for next generation at 7 nm• will require new transistor geometries and probably
new materials• hard to predict beyond that• fabrication becoming very expensive, affordable by only
a few companies• more cores on chip network on chip issues
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Instruction Level Parallelism• Pipelining yields substantial speed-up
• reduces CPI to close to 1
• Dynamic instruction scheduling produces modest further gain
• CPI rarely below 0.5 (Text Fig. 4.78)0
• limited by difficulty of branch predictionand by cache misses (Text Fig. 4.79, 5.46)
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Computer Architecture lecture 24 5
Memory• Improvements in access time don’t keep up
with other components• fast CPU• slow main memory• very slow disk
• Problem reduced by multilevel caches• high hit rates are crucial to performance• top chips have 4 cache levels• flash memory as cache for disk
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Computer Architecture lecture 24 6
Communication• Communication becomes more of a limiting
factor than computation
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Computer Architecture lecture 24 7
More exotic ideas:
• quantum computing
• approximate computation
• brain-inspired computation
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New Avenues in Computer Architecture
UNIVERSITY OF WISCONSIN-MADISON
Specialized logic in form of Accelerators1.
Exploiting Approximate Computing2.
http://www.purdue.edu/newsroom/releases/2013/Q4/approximate-computing-improves-efficiency,-saves-energy.html
As the clock frequency of silicon chips is leveling off, the computer architecture community is looking for different solutions to continue application performance scaling.
Designed to perform special tasks efficiently compared to GPPs.
Specialization leads to better efficiency by trading off flexibility for leanerlogic and hardware resources
Today's computers are designed to compute precise results even when it is not necessary.
Approximate computing trades off accuracy to enable novel optimizations
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Approximate Computing ?500 / 21 = ?
Is it greater than 1 ? Is it greater than 30? Is it greater than 23?
Filtering based on precise calculations Filtering based on approx. calculations
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Approximate Computing• Identify cases where error can be tolerated
• video rendering• image and speech recognition• web search
• Calculate approximate result• use fewer bits• replace exact calculation with learned model
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Computer Architecture lecture 24 11
• Is 'Good Enough' Computing Good Enough?By Logan Kugler Communications of the ACM, Vol. 58 No. 5, Pages 12-14
• 10.1145/2742482• http://cacm.acm.org/magazines/
2015/5/186012-is-good-enough-computing-good-enough/fulltext
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Brain-inspired Computing• Computation based on artificial neural
network
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Multi-layer Perceptron (MLP)- > Artificial Neural Network (ANN) model.
- > Maps sets of input data onto a set of appropriate outputs
- > MLP utilizes a supervised learning technique called backpropagation for training the network
- > The goal of any supervised learning algorithm is to find a function that best maps a set of inputs to its correct output.
typically usenon-linearweighted sumat each node
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Multi-layer Perceptron (MLP)
1. Send the MLP an input pattern, x, from the training set.
2. Get the output from the MLP, y.
3. Compare y with the “right answer”, or target t, to get the error quantity.
4. Use the error quantity to modify the weights, so next time y will be closer to t.
5. Repeat with another x from the training set.
General idea of supervised learning
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Brain-inspired Computing• Human brain
• 10 billion neurons• 100 trillion synapses
• Latest IBM neuromorphic chip• 4 K neurosynaptic cores• 1 million programmable neurons• 256 million adjustable synapses• 5 billion transistors
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Computer Architecture lecture 24 16
• Science 8 August 2014: Vol. 345 no. 6197 pp. 668-673 DOI: 10.1126/science.1254642
• REPORT A million spiking-neuron integrated circuit with a scalable communication network and interface
• http://www.sciencemag.org/content/345/6197/668
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