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An 8b-Precision 6T SRAM Computing-in-Memory Macro Using Time-Domain Incremental Accumulation for AI Edge Chips

Ping-Chun Wu, Jian-Wei Su, Yen-Lin Chung, Li-Yang Hong, Jin-Sheng Ren, Fu-Chun Chang, Yuan Wu, Ho-Yu Chen, Chen-Hsun Lin, Hsu-Ming Hsiao, Sih-Han Li, Shyh-Shyuan Sheu, Shih-Chieh Chang, Wei‐Chung Lo, Chih‐I Wu, Chung‐Chuan Lo, Ren-Shuo Liu, Chih-Cheng Hsieh, Kea‐Tiong Tang, Meng‐Fan Chang

2023IEEE Journal of Solid-State Circuits21 citationsDOI

Abstract

This article presents a novel static random access memory computing-in-memory (SRAM-CIM) structure designed for high-precision multiply-and-accumulate (MAC) operations with high energy efficiency (EF), high readout accuracy, and short compute latency. The proposed device employs 1) a time-domain incremental-accumulation (TDIA) scheme to enable high-accumulation MAC operations while maintaining a large signal margin across MAC values (MACVs), 2) a dynamic differential-reference (D2REF) scheme based on software-hardware co-design to reduce read energy consumption, and 3) a low-dMACV-aware recursive time-to-digital converter (LMAR-TDC) for implementation with the D2REF scheme to further suppress readout energy consumption. A 28 nm 1 Mb SRAM-CIM macro fabricated using foundry-provided compact 6T-SRAM cells achieved EF of 39.31 TOPS/W and compute latency of 6.6 ns for 8b-MAC operations with 64 accumulations per cycle and near-full output precision (22b).

Topics & Concepts

Static random-access memoryLatency (audio)Computer scienceComputer hardwareEmbedded systemEnergy consumptionMacroScheme (mathematics)Access timeRandom access memoryParallel computingEngineeringElectrical engineeringMathematicsMathematical analysisTelecommunicationsProgramming languageAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesSemiconductor materials and devices