Analytically Modeling Power and Performance of a CNN System

Cellular neural networks (CNNs) are a powerful analog architecture that can outperform traditional von Neumann architecture for spatio-temporal information processing applications, e.g., image processing and speech recognition. Much existing work reports energy dissipation for CNNs at the chip level, which includes dissipation of sensors, actuators, and other components. As such, the impacts of various system variables, e.g., application templates, characteristics of the resistive element, etc., on the energy profile of a CNN cannot be easily determined. In this work, we propose analytical models to estimate CNN power and performance (measured by settling time). Power dissipations, and settling times obtained via the models for different linear, and non-linear characteristics are verified through circuit simulation. Simulation results show that the proposed models predict power dissipation and settling time with less than 1% and 3% errors, respectively. By using these models, we have also performed case studies for a tactile sensing problem, and a pattern recognition problem to compare power and performance between tunneling field effect transistor (TFET) based non-linear CNN and conventional linear resistor based CNN.