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  4. Compute-In-Memory Using 6T SRAM for a Wide Variety of Workloads
 
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Compute-In-Memory Using 6T SRAM for a Wide Variety of Workloads

Source
Proceedings IEEE International Symposium on Circuits and Systems
ISSN
02714310
Date Issued
2022-01-01
Author(s)
Bharti, Pramod Kumar
Jain, Saurabh
Pillai, Kamlesh R.
Sayyaparaju, Sagar Varma
Kalsi, Gurpreet S.
Mekie, Joycee  
Subramoney, Sreenivas
DOI
10.1109/ISCAS48785.2022.9937781
Volume
2022-May
Abstract
This paper presents a split wordline 6T SRAM based compute-in-memory subarray with variable multi-bit precision for input operands and outputs. The split wordlines of the 6T cell enable sign segregation, thus allowing arbitrary sign/magnitude multiply and accumulate (MAC) operations. The arbitrary signmagnitude MAC operation extends the usage of the MAC array for DSP workloads as well. On top of that, the split wordline 6T cell is more resilient to write-disturb, which is a major concern for conventional 6T cell based compute-in-memory operation. The proposed system was designed and implemented using 65nm UMC technology and the energy efficiency and throughput are found to be 80.1 TOPS/W and 496 GOPS respectively, for maximum precision of inputs(4 bits)/outputs(5 bits). The maximum throughput and energy efficiency of the design are found to be 780 GOPS and 94.8 TOPS/W respectively. Hand-written digit recognition application mapped on the proposed system showed a maximum accuracy degradation of 0.2% as compared to that obtained from software with the same input/output quantization.
Unpaywall
URI
https://d8.irins.org/handle/IITG2025/26271
Subjects
broad-purpose processing | compute-in-memory | Convolutional neural networks | digital signal processing
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