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Design Tradeoffs of Approximate Analog Neural Accelerators

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### Design Tradeoffs of Approximate Analog Neural Accelerators

Neural-Inspired Accelerators for Computing - January 22, 2013

Renée St. Amant, HadiEsmaeilzadeh, Adrian Sampson, ArjangHassibi, Luis Ceze, Doug Burger

Technology Trends

- Shrinking transistors are less reliable
- Leakage, variation, noise, faults
- Precise computation more expensive
- Motivates research in approximate computing
- End of Dennardscaling
- Dark silicon motivates research in acceleration

Opportunity

- Approximate computing – precise results not required
- trade accuracy for energy efficiency
- Analog circuits trade accuracy for efficiency
- Emerging applications are error-tolerant
- Machine learning, gaming, sensor data processing, augmented reality, etc., etc.

Outline

- Context / Background
- Translates general-purpose, approximation-tolerant code segments to neural networks
- Analog Neural Acceleration
- Opportunity, tradeoffs, and challenges unique to analog!
- Related Work
- Conclusion

Context / Background [Esmaeilzadeh et al., MICRO’12]

- Learning approach to accelerating approximate programs
- Goal: accelerate error-tolerant portions of general-purpose code
- Code transformation to neural network
- Accelerated execution on Neural Processing Unit (NPU)

Neural Processing Unit (NPU)

Compute outputs for various

network topologies

Configuration of PEs,

Storage, Control

PE

Processing Element (PE)

Results [Esmaeilzadeh et al., MICRO’12]

- 2.3x application speedup, 3x energy reduction on average
- Ideal NPU (potential for analog): 3.4x speedup, 3.7x energy improvement on average

Outline

- Context / Background
- Analog neural acceleration
- Relevant design components
- Tradeoffs and challenges
- Preliminary design
- Preliminary results
- Related Work
- Conclusion

Design Space of Neural Processing Units

- Analog presents opportunity for increased energy savings

Flexibility

Accuracy

Efficiency

Design Components to Balance Flexibility, Accuracy, and Efficiency

Potential!

Analog Neural Processing Unit (ANPU)

Efficiently and accurately

compute outputs for various

network topologies

Configuration of APEs,

Storage, Control

APE

Analog Processing Element (APE)

Analog/Digital Boundary

Digital

APE

Analog

- Analog computation is cheap!
- Conversions are expensive!
- Boundary affects flexibility
- Robustness to noise
- Fan out

Analog

Digital

Analog

Digital

Opportunity: Analog Storage

APE Configuration

Map various topologies to one substrate

APE

APE

APE

APE

2

3

1

- Time-multiplexed vs. geometric approach
- Analog efficiency with simultaneous computation

APE Configuration

Map various topologies to one substrate

APE

APE

APE

APE

Analog outputs

fed to next layer

2

3

1

APE

APE

APE

APE

- Time-multiplexed vs. geometrical layout
- Analog efficiency with simultaneous computation
- Fixed computation width
- Challenge: Range!
- Larger range decrease circuit accuracy
- Maximize efficient simultaneous computation, maintaining accuracy
- Row width (connections) – hardware / software accuracy tradeoff

Value Representation

- Represent values – inputs, weights, intermediates
- Current? voltage? Some combination? One or more wires?
- Analog computation circuits have favorites
- Signal type
- Signal range
- Affect accuracy, efficiency
- Cost of conversion and scaling vs. computation accuracy and efficiency

Value Representation: Bit Width

- Number of bits of inputs, weights, outputs
- Implications on power
- Hardware / software accuracy
- More bits, more accuracy?
- Challenge: Range!

APE

Outline

- Context / Background
- Analog neural acceleration
- Overview
- Relevant design components
- Preliminary APE design
- Preliminary results
- Related Work
- Conclusion

Analog Processing Element Design

Weight 0

Input 0

Weight 1

Input 1

Weight 7

Input 7

Current Steering DAC

Ibias

I+

I-

V+

Resistor Ladder (DAC)

V-

Ibias/2 + ΔI

Ibias/2 - ΔI

MUL

ADD

ADC

Output

Clock

Circuit Power, Accuracy, and Delay

- 8-wide APE, 5-bit inputs, 4-bit weights
- Power = 23 mW
- Error below one quantization step at 1.67 GHz

Input Bit Width and Energy

*

*

*

*

* S. Galal and M. Horowitz. Energy-efficient floating-point unit design. IEEE Trans. Comput., 60(7):913–922, 2011.

APE input bit-width has exponential effect on energy consumption

Bit Width and Potential Accuracy

APE input bit-width and weight bit-width affect achievable accuracy

Range

Ibias

V+

V-

I-

I+

Output Bits

1 uA 6 bits

10 uA 6 bits, 7 bits?

100 uA 8 bits

- Strategy: increase “linear” range
- Hardware / software accuracy tradeoff
- Answers at the application level
- Exponential increase in computation power for linear increase in output bits

Outline

- Context / Background
- Analog neural acceleration
- Related Work
- Conclusion

Related Work – Approximate Computing

- Digital hardware techniques [PCMOS]
- Limited benefit
- Analog hardware techniques
- Lack successful integration with high-performance CPU
- Approximate programming models [EnerJ]
- ANPU is an implementation of approximate computing

Related Work – Hardware Neural Networks

- Most prior work on analog neural networks
- Small network, not designed to be fast, old technology, targets very specific applications
- More recent work
- SpiNNaker, IBM’s Cognitive Chip, ByMoore, FACETS, neuFlow
- Goal?
- Could be considered for NPU implementations

Conclusion

- Analog, fine-grained knobs balance flexibility, accuracy, and efficiency
- Hardware / software accuracy tradeoff
- Challenge: Watch your range!
- Work in Progress
- Circuit-level accuracy application-level accuracy
- Digital / analog boundaries and opportunity analog storage
- Open questions: Noise?
- Important with the rise of error-tolerant applications

Questions?

- Suggestions? Feedback?
- [email protected]
- Thank you!

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