Value-Based Deep-Learning Acceleration

This article summarizes our recent work on value-based hardware accelerators for image classification using Deep Convolutional Neural Networks (CNNs). The presented designs exploit runtime value properties that are difficult or impossible to discern in advance. These include values that are zero or near zero and that prove ineffectual, have reduced yet variable precision needs, or have ineffectual bits. The designs offer a spectrum of choices in terms of area cost, energy efficiency, and relative performance when embedded in server class installations. More importantly, the accelerators reward advances in CNN design that increase the aforementioned properties.