A Pulse Width Modulation based Power-elastic and Robust Mixed-signal Perceptron Design

TL;DR

This paper introduces a novel mixed-signal perceptron design based on pulse width modulation that is highly power-elastic and robust against variations, suitable for self-powered micro-edge AI systems.

Contribution

It presents a PWM-based mixed-signal perceptron architecture that enhances power elasticity and robustness in neural network circuits for energy-harvesting applications.

Findings

Demonstrates high resilience to power and parameter variations

Uses Cadence tools for extensive design analysis

Validates robustness with a 3x3 perceptron case study

Abstract

Neural networks are exerting burgeoning influence in emerging artificial intelligence applications at the micro-edge, such as sensing systems and image processing. As many of these systems are typically self-powered, their circuits are expected to be resilient and efficient in the presence of continuous power variations caused by the harvesters. In this paper, we propose a novel mixed-signal (i.e. analogue/digital) approach of designing a power-elastic perceptron using the principle of pulse width modulation (PWM). Fundamental to the design are a number of parallel inverters that transcode the input-weight pairs based on the principle of PWM duty cycle. Since PWM-based inverters are typically agnostic to amplitude and frequency variations, the perceptron shows a high degree of power elasticity and robustness under these variations. We show extensive design analysis in Cadence Analog Design Environment tool using a 3x3 perceptron circuit as a case study to demonstrate the resilience in the presence of parameric variations.