This brief presents an in-memory compute macro for neural-network based controllers
including inference and in-situ weight updates featuring:(1) in-memory multi-bit matrix and …

The second wave of AI is about statistical learning of low dimensional structures from high
dimensional data. Inference is done using multilayer, data transforming networks using fixed …

In this article, we introduce a novel technique called E-multiplication and accumulation
(MAC)(EMAC), aimed at enhancing energy efficiency, reducing latency, and improving the …

In-SRAM computing promises energy efficiency, but circuit nonlinearities and PVT variations
pose major challenges in designing robust accelerators. To address this, we introduce …

This paper presents a novel circuit (AID) to improve the accuracy of an energy-efficient in-
memory multiplier using a standard 6T-SRAM. The state-of-the-art discharge-based in …

Motivated by the parsimony and robustness of the biological brain at computing, engineers
have, over the last four decades, been emulating neurophysiology in silicon, ie …

This paper presents the design of a low power multiplier cell which multiplies an input
voltage with current. The multiplier achieves high precision with±0.1% error for a wide range …

State-of-the-art In-memory processing has recently emerged as the most promising solution
to overcome design challenges related to data movement inside current computing systems …

In this work, a mixed-signal architecture, inspired from the widely-popular successive
approximation analog-to-digital converter, is presented as an energy-efficient alternative to …

Matched filtering is used in radar signal processing for optimal detection of signals in highly
noisy environments. In this paper, we demonstrate a novel method for matched matched …