Neuro-CIM: A 310.4 TOPS/W Neuromorphic Computing-in-Memory Processor with Low WL/BL activity and Digital-Analog Mixed-mode Neuron Firing
Sangyeob Kim, Sangjin Kim, Soyeon Um, Soyeon Kim, Kwantae Kim, Hoi‐Jun Yoo
20222022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)24 citationsDOI
Abstract
An energy-efficient neuromorphic computing-in-memory (CIM) processor is proposed with four key features: 1) Most significant bit (MSB) Word Skipping to reduce the BL activity; 2) Early Stopping to enable lower BL activity; 3) Mixed-mode firing for multi-macro aggregation; 4) Voltage Folding to extend the dynamic range. The proposed CIM achieves state-of-the-art energy efficiency of 62.1 TOPS/W (I=4b, W=8b) and 310.4 TOPS/W (I=4b, W=1b).
Topics & Concepts
Neuromorphic engineeringTOPSComputer scienceMode (computer interface)8-bitBlock (permutation group theory)Efficient energy useFolding (DSP implementation)Word (group theory)Computer architectureComputer hardwareElectrical engineeringArtificial neural networkEngineeringArtificial intelligenceOperating systemMathematicsMechanical engineeringGeometrySpinningAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeural Networks and Reservoir Computing