Energy-Efficient Moderate Precision Time-Domain Mixed-signal Vector-by-Matrix Multiplier Exploiting 1T-1R Arrays
Shubham Sahay, Mohammad Bavandpour, Mohammad Reza Mahmoodi, and Dmitri, Strukov

TL;DR
This paper introduces a novel time-domain mixed-signal vector-by-matrix multiplier using 1T-1R arrays, achieving high energy efficiency and moderate precision suitable for IoT and neuromorphic applications.
Contribution
It presents the first time-domain mixed-signal VMM leveraging 1T-1R arrays, overcoming energy inefficiencies of current approaches, and provides design guidelines for optimizing precision and efficiency.
Findings
Achieves approximately 6-bit computational precision.
Demonstrates a 200x200 VMM with ~1.5 POps/J energy efficiency.
Provides insights into non-ideal factors affecting precision.
Abstract
The emerging mobile devices in this era of internet-of-things (IoT) require a dedicated processor to enable computationally intensive applications such as neuromorphic computing and signal processing. Vector-by-matrix multiplication (VMM) is the most prominent operation in these applications. Therefore, there is a critical need for compact and ultralow-power VMM blocks to perform resource-intensive low-to-moderate precision computations. To this end, in this work, for the first time, we propose a time-domain mixed-signal VMM exploiting a modified configuration of 1MOSFET-1RRAM (1T-1R) array. The proposed VMM overcomes the energy inefficiency of the current-mode VMM approaches based on RRAMs. A rigorous analysis of the different non-ideal factors affecting the computational precision indicates that the non-negligible minimum cell currents, channel length modulation (CLM) and…
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