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COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J . Leverett A. Pr att R. H o chman May 2013 – EE241 Final Project

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Page 1: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING

J. L

ever

ett

A. Pratt

R. H

ochm

an

May 2013 – EE241 Final Project

Page 2: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Introduction to ‘Spikes’Frequency Range 10-500Hz

Duration 3-5ms

Resolution 8-bit

‘Spikes,’also called ‘action potentials,’ are when the electrical potential of a neuron rises shortly. It is part of the communication protocol of the brain.

Page 3: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Spike Sorting Systems

• Operate at constant 37°C • Extremely low power• Extremely low area

Page 4: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Purpose Statement

Adaptive Voltage and Frequency Scaling Asynchronous Timing

vs.

Determine the conditions

• under which adaptive voltage and frequency scaling provides a better power performance than asynchronous timing in a neural spike processor.

• under which asynchronous timing provides a better power performance than adaptive voltage and frequency scaling in a neural spike processor.

Page 5: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Theory• E = I*VDD*t• If you can decrease voltage and/or current faster than the increase in delay for an operation, you save

energy.• Asynchronous circuits process data at the same energy ‘every time.’ • When data rates are low, I and VDD may be reduced such that the energy per spike of a synchronous

solution is lower than the asynchronous solution.• The issue is that at some point, leakage current dominates, making increased operating time

undesirable.

Page 6: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Verilog Function

• Detection of the largest value in a spike and the point at which it occurs facilitates spike sorting.

• Sorting spikes on chip results in a reduced amount of transmitted data.

Page 7: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Synchronous Logic

?

Page 8: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Prior Art

Page 9: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Asynchronous Logic

Page 10: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Data Collection Methodology

Determination of max. frequency and E vs. VDD.

Known Simplifications• Process Corners• Entire Verification Space• Noise/Margins• Critical Path Monitors• Adaptive control circuitry

Synthesis

Place and Route

.sp netlist

E vs. f Data

Verilog Cadence hSpice Matlab

sim

sim

sim

LVS sim

*RC

Parasitic extraction

Logic

Demonstrations of functionalitywith synch and asynch.

Page 11: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Spice Data: Energy/Op vs. Frequency

Asynchronous:300 less MOSFETS>5000 less Rs and Cs

At 20kHz: Synch: 1.9E-9A – 7.6pJ Asynch: 1.71E-9A, 6.8pJ

Page 12: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Spice Data: Current Breakdown - Synchronous

Page 13: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Assumptions• Process Corners

• Only used TT as effects should be linear on both architectures.

• Verification Space• Test vector data was designed to be representative, not the worse

case scenario. Testing across a wide range of frequencies and voltages is an open problem.

• Noise/Margins• Assumed no noise for this analysis. Noise should hurt both circuits

equally.

Page 14: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

• Subthreshold critical path monitors are highly sensitive to PVT both globally and locally.

• Current is exponential with changes in , , and T.

• Implies a currently unachievable level of specificity.

• First order approximation might be to double energy.

Critical Path Monitor

Adaptive Control Circuitry, Clock, + Vdd •Assuming that the adaptive frequencies are less than the global clock, we can implement the change in frequency with a clock divider (counter).

•Voltage scaling circuitry would take the global supply and reduce it using one of many possible techniques. –Linear Regulators: Inefficient for large changes in voltage.–Capacitive Regulator: Results in supply ripples.–Magnetic Regulator: Highest efficiency, but requires external parts

Page 15: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Matlab: Transient State Machine at .5MHz

Page 16: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Matlab: Transient State Machine at .6Mhz

Page 17: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Matlab: Transient State Machine at 5Mhz

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Matlab: Total Energy + Vdd vs. Event Frequency

Page 19: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

Conclusion• Asynchronous logic operates at a higher frequency for all

supply values than the synchronous version. Similarly, for an equal operating frequencies, asynchronous can operate at a lower VDD.

• For the speed of a neural processor, both circuits operate at the lowest supply voltage and leakage current dominates. There is no means to perform adaptive analysis.

• In an application where incoming data frequency is on the order of magnitude of the intrinsic asynchronous circuit operating frequency, the adaptive circuit requires less energy, as long as the average voltage required of the adaptive circuit is less than the intrinsic voltage of the asynchronous circuit.

Page 20: COMPARISON OF ADAPTIVE VOLTAGE/FREQUENCY SCALING AND ASYNCHRONOUS PROCESSOR ARCHITECTURES FOR NEURAL SPIKE SORTING J. Leverett A. Pratt R. Hochman May

References and Acknowledgements

Acknowledgements: Brian Zimmer, Nathan Narevsky, Jan Rabaey, Stevo Bailey, Tsung-Te Liu, Bora Nikolic

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APPENDIX

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Spice Data: Power vs. Max Operating Freq.