PatterNet: Explore and Exploit Filter Patterns for Efficient Deep Neural Networks
TimeTuesday, July 12th2:15pm - 2:37pm PDT
Location3002, Level 3
AI/ML Design: Circuits and Architecture
DescriptionIn this paper, we propose PatterNet, which enforces shared clustering topologies on filters. Cluster sharing leads to a greater extent of memory reduction by reusing the index information. PatterNet effectively factorizes input activations and post-processes the unique weights, which saves multiplications by several orders of magnitude. Furthermore, PatterNet reduces the add operations by harnessing the fact that filters sharing a clustering pattern have the same factorized terms. We introduce techniques for determining and assigning clustering patterns and training a network to fulfill the target patterns. We also propose and implement an efficient accelerator that builds upon the patterned filters.