HC2022.KAIST.SangyeobKim.v1.pdf

1、1 of 25HOTCHIPS 2022Neuro-CIM:A 310.4 TOPS/W Neuromorphic Computing-in-Memory Processor with Low WL/BL activity and Digital-Analog Mixed-mode Neuron FiringNeuro-CIM:A 310.4 TOPS/W Neuromorphic Computing-in-Memory Processor with Low WL/BL activity and Digital-Analog Mixed-mode Neuron FiringSangyeob K

2、im1,Sangjin Kim1,Soyeon Um1,Soyeon Kim1,Kwantae Kim2,and Hoi-Jun Yoo11School of Electrical Engineering,KAIST2Institute of Neuroinformatics,University of Zurich and ETH Zurich2 of 25HOTCHIPS 2022Neuro-CIM:A 310.4 TOPS/W Neuromorphic Computing-in-Memory Processor with Low WL/BL activity and Digital-An

3、alog Mixed-mode Neuron FiringComputing-in-Memory(CIM)Accelerator Multi WLs Driving Low Energy Efficiency by ADC(100 TOPS/W)Cons1 WL Multi Cells Active1 Col.Multi WLs ActiveADC/DAC Large Power Large AreaADCW01W02W0MW10W11W12W1MW20W21W22W2MWN0WN1WN2WNM DACDigital Input MemoryDigital Output MemoryCIM A

4、rchitectureInput MemoryWeightMemoryBottleneckMACMACMACMACMACMACMACMACMACMACMACMACMAC ArrayNPU ArchitectureOutput MemoryXi x WijW00 Xi x WijDigitizationProsMEM Access ReductionAnalog Accumulation3 of 25HOTCHIPS 2022Neuro-CIM:A 310.4 TOPS/W Neuromorphic Computing-in-Memory Processor with Low WL/BL act

5、ivity and Digital-Analog Mixed-mode Neuron FiringLimitation of Previous CIMs1.High Precision ADC is Required for Digital Output Activations2.Low Energy Efficiency due to Low Sparsity in Real ConditionsADC59%Driver21%Controller20%0Energy Efficiency(TOPS/W)2040608010035.675.96.22.85.8Peak Efficiency C

6、IFAR-10 ImageNetISSCC 21ISSCC 204 of 25HOTCHIPS 2022Neuro-CIM:A 310.4 TOPS/W Neuromorphic Computing-in-Memory Processor with Low WL/BL activity and Digital-Analog Mixed-mode Neuron FiringNeuromorphic CIM Processor ADC and DAC are Not Necessary Power/Area Reduction Event-driven operation Input sparsi