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X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories by Agrawal, Amogh; Jaiswal, Akhilesh; Lee, Chankyu; Roy, Kaushik is a scholarly article available to read on EtoBox.

What is X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories about?

Silicon-based Static Random Access Memories (SRAM) and digital Boolean logic have been the workhorse of the state-of-art computing platforms. Despite tremendous strides in scaling the ubiquitous metal-oxide-semiconductor transistor, the underlying \textit{von-Neumann} computing architecture has remained unchanged. The limited throughput and energy-efficiency of the state-of-art computing systems, to a large extent, results from the well-known \textit{von-Neumann bottleneck}. The energy and throughput inefficiency of the von-Neumann machines have been accentuated in recent times due to the present emphasis on data-intensive applications like artificial intelligence, machine learning \textit{etc}. A possible approach towards mitigating the overhead associated with the von-Neumann bottleneck is to enable \textit{in-memory} Boolean computations. In this manuscript, we present an augmented version of the conventional SRAM bit-cells, called \textit{the X-SRAM}, with the ability to perform in-memory, vector Boolean computations, in addition to the usual memory storage operations. We propose at least six different schemes for enabling in-memory vector computations including NAND, NOR, IMP

Author

Agrawal, Amogh; Jaiswal, Akhilesh; Lee, Chankyu; Roy, Kaushik

Published

2017

Language

EN