A Pulse Width Modulation based Power-elastic and Robust Mixed-signal Perceptron Design
Sergey Mileiko1, Rishad Shafik1, Alex Yakovlev1 and Jonathan Edwards2
1Newcastle University, UK
2Temporal Computing, UK
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 3 x 3 perceptron circuit as a case study to demonstrate the resilience in the presence of parameric variations.
