Alternative Computing
Technologies
CS 8803 ACT
Spring 2014
Hadi Esmaeilzadeh
hadi@cc.gatech.edu
Georgia Institute of Technology
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Alternative,Computing,Technologies
Hadi Esmaeilzadeh
From Khoy, Iran
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PhD in CSE, University of Washington
Doug Burger and Luis Ceze
2013 William Chan Memorial Dissertation Award
MSc in CS, The University of Texas at Austin
MSc and BSc in ECE, University of Tehran
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Research: ACT Lab
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Alternative Computing Technologies
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Alternative,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 organization
3. 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 technologies
5. Quiz # 1
5
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
Class Presentation
Class Participation
Critiques
Final Project
Fraction
30%
10%
25%
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 something
interesting!
 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”
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Reading material for writing critiques
The task of the referee
Allen Jay Smith
Style: The Basics of Clarity and Grace
Joseph M. Williams
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 is
2. Course organization
3. 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 technologies
5. 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 architecture
General-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
Fetch
Decode
Reg Read
Branch
Predictor
I Cache
ITLB
Execute
Memory
Write
Back
INT FU
Decoder
D Cache
Register
File
FP FU
Register
File
DTLB
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Programmability versus Efficiency
Programmability
General-Purpose Processors
SIMD Units
GPUs
FPGAs
ASICs
Efficiency
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What is the difference between the
computing industry and the paper
towel industry?
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Industry of replacement
1971
2013
?
Industry of new possibilities
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Can we continue being an
industry of new possibilities?
Personalized
healthcare
Virtual
reality
Real-time
translators
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Agenda
1. Who Hadi is
2. Course organization
3. 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 technologies
5. Quiz # 1
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Moore’s Law
Or, how we became an industry of new possibilities
Every 2 Years
 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×↓)
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What is the catch?
Powering the transistors without melting the chip
10,000,000,000
2,200,000,000
Chip Transistor Count
1,000,000,000
100,000,000
Chip Power
10,000,000
Moore’s Law
1,000,000
100,000
10,000 2300
1,000
130 W
100
10
0.5 W
1
0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
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Dennard scaling:
Doubling the transistors; scale their power down
Transistor: 2D Voltage-Controlled Switch
Dimensions
Voltage
×0.7
Doping
Concentrations
Area
0.5×↓
Capacitance
0.7×↓
Frequency
Power
1.4×↑
Power = Capacitance × Frequency × Voltage2
0.5×↓
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Dennard scaling broke:
Double the transistors; still scale their power down
Transistor: 2D Voltage-Controlled Switch
Dimensions
Voltage
×0.7
Doping
Concentrations
Area
0.5×↓
Capacitance
0.7×↓
Frequency
Power
1.4×↑
Power = Capacitance × Frequency × Voltage2
0.5×↓
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Dark silicon
If you cannot power them, why bother making them?
Area
Power
0.5×↓
0.5×↓
Dark Silicon
Fraction of transistors that need to be
powered off at all times
due to power constraints
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Looking back
Evolution of processors
Dennard scaling
broke
Single-core Era
Multicore Era
3.4 GHz
3.5 GHz
2003
2013
740 KHz
1971
2004
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Are multicores a long-term
solution or just a stopgap?
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Agenda
1. Who Hadi is
2. Course organization
3. 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 technologies
5. Quiz # 1
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Modeling future multicores
Quantify 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-Core
Scaling Model
Multicore
Scaling Model
Esmaeilzadeh, Belem, St. Amant, Sankaralingam, Burger, “Dark Silicon and the End of Multicore Scaling,” ISCA 2011
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Transistor scaling model
From 45 nm to 8 nm
[Dennard, 1974]
[ITRS, 2010]
[VLSI-DAT, 2010]
Historical
Scaling
Optimistic
Scaling Model
Conservative
Scaling Model
Area
32× ↓
32× ↓
32× ↓
Power
32× ↓
8.3× ↓
4.5× ↓
Speed
5.7× ↑
3.9× ↑
1.3× ↑
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Single-core model (45 nm)
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Intel Nehalem
AMD Shanghai
Intel Core
Intel Atom
Pareto Frontier (45 nm)
Core Power (Watts)
25
20
15
10
5
0
0
5
10
15
20
25
Core Performance (SPECmark)
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Power-Performance and Area-Performance
Pareto Optimal Frontiers
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40
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Single-core scaling model
From 45 nm to 8 nm
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Core Power (Watts)
25
20
Transistor Speed Scaling Factor
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Transistor Power Scaling Factor
10
5
0
0
5
10
15
20
25
Core Performance (SPECmark)
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Single-core Scaling Model:
Single-core Model × Transistor Scaling Model
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40
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Multicore scaling model
From 45 nm to 8 nm
Single Core Search Space
(Scaled Area and Power Pareto Frontiers)
Constraints
Application Characteristics
(Area and Power Budget)
(% Parallel, % Memory Accesses)
Multicore Organization:
CPU-Like, GPU-Like
(# of HW Threads, Cache Sizes)
Multicore Topology
Microarchitectural Features
(Symmetric, Asymmetric,
Dynamic, Composable)
(Cache and Memory Latencies, CPI,
Memory Bandwidth)
Exhaustive search of multicore design space
(Examine 800 design points for every technology node)
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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
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2013
Performance Improvement / 45 nm
20
18×
Historical Trend
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Optimistic Transistor Scaling (Projection)
12
Conservative Transistor Scaling (Projection)
8
7.9×
4
3.7×
0
45 nm
32 nm
22 nm
16 nm
11 nm
8 nm
Dark Silicon
10 years
45 nm
32 nm
22 nm
16 nm
11 nm
8 nm
1%
17%
36%
40%
51%
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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 is
2. Course organization
3. 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
technologies
5. Quiz # 1
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Alternative computing
technologies
Approximate Computing
Analog Computing
Biological Computing
Neuromorphic Computing
Stochastic Computing
Human-based
Computing
Perpetual Computing
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Approximate computing
Embracing 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 computing
Personalized and targeted computing
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Classes of
approximate 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 dimension
Embracing Error
Energy
Processor
Pareto.Fron0er
Performance
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A fertile ground for innovation
Energy
Processor
Pareto.Fron0er
Performance
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Approximate computing techniques
Same Model
• Sampling
– Loop perforation (MIT)
From Model to Model
• von Neumann to Neural
– NPUs (UW, GaTech)
• Compression
– Sage (Michigan)
• Early termination
– Green (MSR)
• Replacement
– Green (MSR)
• Lower voltage
– Truffle (Rice, UW)
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Analog Computing
Computing with Physics
http://youtu.be/dAyDi1aa40E
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Agenda
1. Who Hadi is
2. Course organization
3. 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 technologies
5. Quiz # 1
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