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Union SRAM: PVT Variation Auto-Compensated, Bit Precision Configurable Current Mode 8T SRAM in Memory MAC Macroopen access

Authors
Kim, HongguAn, YerimKim, RyunyeongKim, SunyoungShim, Yong
Issue Date
2024
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Random access memory; In-memory computing; Voltage; Computer architecture; Common Information Model (computing); Throughput; SRAM cells; Robustness; Artificial neural networks; Operational amplifiers; SRAM compute-in-memory; DNN accelerator; PVT auto-compensated MAC macro
Citation
IEEE ACCESS, v.12, pp 162882 - 162893
Pages
12
Journal Title
IEEE ACCESS
Volume
12
Start Page
162882
End Page
162893
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/77787
DOI
10.1109/ACCESS.2024.3487975
ISSN
2169-3536
2169-3536
Abstract
SRAM-based Compute-In-Memory (CIM) has two main paradigms: Digital domain and Analog domain, where both have been extensively explored to overcome the von-Neumann bottleneck and enhance energy efficiency. Digital CIM offers robustness and dynamic bit-precision through bit-wise and bit-serial computing, but suffers from limited throughput due to multi-cycle operations and degraded area density due to large hardware footprints. In contrast, Analog CIM offers the significantly improved throughput and area density for the analog computing nature and simple logic structure. However, the weight and input data are constrained by fixed bit-precision, limiting the flexibility in DNN applications. Additionally, Analog CIM is susceptible to process, voltage, and temperature (PVT) variations, resulting in potential accuracy degradation. We present a solution to the limitations of analog domain SRAM CIM in dynamic bit-precision configurability and PVT variation vulnerability. Our proposed 4b/8b bit-precision configurable analog current-mode 8T SRAM CIM architecture enhances DNN application flexibility. We also introduce PVT variation auto-compensation scheme, effectively maintaining precise computing accuracy of the analog domain CIM. Post-layout simulations confirm the architecture's efficacy, achieving a throughput of 170 to 793.4 GOPs, area efficiency of 0.227 to 1.06 TOPs/mm2, and energy efficiency of 5.1 to 23.76 TOPs/W. Additionally, software-level simulation on the CIFAR-10 dataset demonstrates 95.02 percent classification accuracy.
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창의ICT공과대학 (전자전기공학부)
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