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Computational SRAM (C-SRAM) Solution Combining In- and Near-Memory Computing Approaches

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New academic paper titled “Towards a Truly Integrated Vector Processing Unit for Memory-bound Applications Based on a Cost-competitive Computational SRAM Design Solution”, from researchers at Univ. Grenoble Alpes, CEA-LIST.

Abstract

“This article presents Computational SRAM (C-SRAM) solution combining In- and Near-Memory Computing approaches. It allows performing arithmetic, logic, and complex memory operations inside or next to the memory without transferring data over the system bus, leading to significant energy reduction. Operations are performed on large vectors of data occupying the entire physical row of C-SRAM array, leading to high performance gains. We introduce the C-SRAM solution in this article as an integrated vector processing unit to be used by a scalar processor as an energy-efficient and high performing co-processor. We detail the C-SRAM system design on different levels: (i) circuit design and silicon proof of concept, (ii) system interface and instruction set architecture, and (iii) high-level software programming and simulation. Experimental results on two complete memory-bound applications, AES and MobileNetV2, show that the C-SRAM implementation achieves up to 70× timing speedup and 37× energy reduction compared to scalar architecture, and up to 17× timing speedup and 5× energy reduction compared to SIMD architecture.”

Find the open access technical paper here. Published April 2022.

Maha Kooli, Antoine Heraud, Henri-Pierre Charles, Bastien Giraud, Roman Gauchi, Mona Ezzadeen, Kevin Mambu, Valentin Egloff, and Jean-Philippe Noel. 2022. Towards a Truly Integrated Vector Processing Unit for Memory-bound Applications Based on a Cost-competitive Computational SRAM Design Solution. J. Emerg. Technol. Comput. Syst. 18, 2, Article 40 (April 2022), 26 pages. https://doi.org/10.1145/3485823.

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