[J11] A Charge-Domain Scalable-Weight In-Memory Computing Macro with Dual-SRAM Architecture for Precision-Scalable DNN Accelerators

Abstract

This paper presents a charge-domain in-memory computing (IMC) macro for precision-scalable deep neural network accelerators. The proposed Dual-SRAM cell structure with coupling capacitors enables charge-domain multiply and accumulate (MAC) operation with variable-precision signed weights. Unlike prior charge-domain IMC macros that only support binary neural networks or digitally compute weighted sums for MAC operation with multi-bit weights, the proposed macro implements analog weighted sums for energy-efficient bitscalable MAC operations with a novel series-coupled merging scheme. A test chip with a 16-kb SRAM macro is fabricated in 28- nm FDSOI process, and the measured macro throughput is 125.2- 876.5 GOPS for weight bit-precision varying from 2 to 8. The macro also achieves energy efficiency ranging from 18.4 TOPS/W for 8–b weight to 119.2 TOPS/W for 2-b weight.

Publication
IEEE TCAS-I
Johnny Rhe (이존이)
Johnny Rhe (이존이)
Combined MS-PhD student
Jaehyun Park (박재현)
Jaehyun Park (박재현)
NC Soft (엔씨소프트)