SeFAct2: Selective Feature Activation for Energy-Efficient CNNs using Optimized Thresholds

This work presents a framework for dynamic energy reduction in hardware accelerators for convolutional neural networks (CNNs). The key idea is based on the early prediction of the features that may be important, with the deactivation of computations related to unimportant features and static bitwidth reduction. The former is applied in late layers of the CNN, while the latter is more effective in ...