Thermodynamic Cost of Edge Detection in Artificial Neural Network(ANN)-Based Processors

Architecture-based heat dissipation analyses allows us to reveal fundamental sources of inefficiency in a given processor and thereby provide us with roadmaps to design less dissipative computing schemes independent of technology-bases used to implement the processor. In this work, we study architectural-level contributions to energy dissipation in Artificial Neural Network (ANN)-based processors that are trained to perform edge detection task. We compare the training and information processing cost ofANNs to that of conventional architectures and algorithms using 64-pixel binary image. Our results reveal the inherent efficiency advantages of ANN networks trained for specific tasks over general purpose processors based on von Neumann architecture.We also compare the proposed performance improvements to that of CAPs and show the reduction in dissipation for special purpose processors. Lastly, we calculate the change in dissipation as a result of change in input data structure and show the effect of randomness on energetic cost of information processing. The results we obtain provide a basis for comparison for task-based fundamental energy efficiency analyses for a range of processors and therefore contribute to the study of architecture-level descriptions of processors and thermodynamic cost calculations based on physics of computation.