Inner Product Computation In-Memory Using Distributed Arithmetic

Lakshmi V, Pudi V, Reuben JR (2022)

Publication Type: Journal article

Publication year: 2022

Journal

IEEE Transactions on Circuits and Systems I-Regular Papers Institute of Electrical and Electronics Engineers (IEEE)

Pages Range: 1-12

DOI: 10.1109/TCSI.2022.3193678

Abstract

In-memory computing using emerging technologies such as Resistive Random-Access Memory (ReRAM) has been proposed as a promising substitute for future computing applications to address the ‘von Neumann bottleneck’. Multiplication is the key component for inner product computation in every digital signal processing (DSP) application and the complexity of multipliers increases greatly with bit-width. Distributed arithmetic (DA) using look-up tables and adder-shifter module has been proposed for inner product computation to achieve multiplier-less efficient DSP architectures, particularly when one of the vectors is a constant and known in advance. Due to the memory wall, DA can be made furthermore latency and energy-efficient when implemented ‘in memory’. In this work, for the first time, we propose two design techniques to compute inner product completely in memory using DA. This is accomplished by storing the precomputed look-up table contents in a ReRAM array and implementing adder-shifter module also in the same array. The adder-shifter is implemented in memory using majority gates which are in turn realized as READ operations in the memory array. Two methods of mapping: latency-optimized and area-optimized and their comparison in terms of latency and area are presented. The proposed method-1 achieves $\approx$ 60% energy savings compared to CMOS and the proposed method-2 achieves 10.59 times higher throughput compared to CMOS.

Authors with CRIS profile

John Reuben Prabahar Lehrstuhl für Informatik 3 (Rechnerarchitektur)

Involved external institutions

Indian Institute of Technology Tirupati (IITTP) / ఇండియన్ ఇన్స్టిట్యూట్ ఆఫ్ టెక్నోలజీ తిరుపతి IN India (IN)

How to cite

APA:

Lakshmi, V., Pudi, V., & Reuben, J.R. (2022). Inner Product Computation In-Memory Using Distributed Arithmetic. IEEE Transactions on Circuits and Systems I-Regular Papers, 1-12. https://dx.doi.org/10.1109/TCSI.2022.3193678

MLA:

Lakshmi, Vijaya, Vikramkumar Pudi, and John Reuben Reuben. "Inner Product Computation In-Memory Using Distributed Arithmetic." IEEE Transactions on Circuits and Systems I-Regular Papers (2022): 1-12.

BibTeX: Download