Hardware-aware Pruning of DNNs using LFSR-Generated Pseudo-Random Indices

Understand

Deep neural networks (DNNs) have been emerged as the state-of-the-art algorithms in broad range of applications.

• To reduce the memory foot-print of DNNs, in particular for embedded applications, sparsification techniques have been proposed.
• Unfortunately, these techniques come with a large hardware overhead.
• In this paper, we present a hardware-aware pruning method where the locations of non-zero weights are derived in real-time from a Linear Feedback Shift Registers (LFSRs).