SeFAct - selective feature activation and early classification for CNNs.

This work presents a dynamic energy reduction approach for hardware accelerators for convolutional neural networks (CNN). Two methods are used: (1) an adaptive data-dependent scheme to selectively activate a subset of all neurons, by narrowing down the possible activated classes (2) static bitwidth reduction. The former is applied in late layers of the CNN, while the latter is more effective in early layers. Even accounting for the implementation overheads, the results show 20%--25% energy savings with 5--10% accuracy loss.