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.
@article{arxiv.1910.08426,
title = {A Pulse Width Modulation based Power-elastic and Robust Mixed-signal Perceptron Design},
author = {Sergey Mileiko and Rishad Shafik and Alex Yakovlev and Jonathan Edwards},
journal= {arXiv preprint arXiv:1910.08426},
year = {2019}
}
Comments
arXiv admin note: text overlap with arXiv:1910.07492