Abstract:
This paper presents a Digital Compute-In-Memory design in 12nm FinFET technology with capacity to store weights for 16 kernels per input channel. This macro is designed u...Show MoreMetadata
Abstract:
This paper presents a Digital Compute-In-Memory design in 12nm FinFET technology with capacity to store weights for 16 kernels per input channel. This macro is designed using an 8T SRAM push-rule foundry bitcell with integrated kernel selection and multiplication for an AI Edge application that achieves 30% better TOPS/mm2 without loss of TOPS/W than a comparable logic-rule custom bitcell based architecture on the same silicon. We present novel power saving architectures, Activation Based Precharge and Folded Kernel Selector that achieves 11.2 TOPS/mm2 and 137 TOPS/W with highest reported 16 kernels per input channel. Further, we showcase novel design circuitry to reduce peak current by 69.1% using a new Precharge During Write scheme.
Date of Conference: 11-16 June 2023
Date Added to IEEE Xplore: 24 July 2023
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