Dynamics of Time-Domain Power-Elastic Circuits for Pervasive Machine Learning

Time-domain data encoding, in the form of the duty cycle of a pulse width modulated (PWM) signal, has recently shown promising ways of building Machine Learning (ML) circuits. As the temporal signals approximately retain their “pseudo-analog” capacitive charging rates under voltage/ frequency variations, the circuits designed are inherently power elastic, offering the crucial leverage of energy autonomy for pervasive applications. This paper focuses on the analysis of dynamic parametric variations and their impact on the temporally encoded Machine Learning circuits. The aim is to investigate and suitably optimize these parameters for robustness, power elasticity and energy efficiency. Our study of dynamics includes how the selection of passive (R and C) components affects the dynamic range of operating frequency, which we term as “PWM carrier frequency”. We investigate how RC values define the performance and energy in terms of computation latency and energy per operation. Additionally, we demonstrates how the dynamic range of voltage and frequency variations affect functional and non-functional parameters of the PWM-based neural network solutions.