  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.
