Using Network Architecture Search for Optimizing Tensor Compression

Abstract

In this work we propose to use Network Architecture Search (NAS) for controlling the per layer parameters of a Tensor Compression (TC) algorithm using Tucker decomposition in order to optimize a given convolutional neural network for its parameter count and thus inference performance on embedded systems. TC enables a quick generation of the next instance in the NAS process, avoiding the need for a time consuming full training after each step. We show that this approach is more efficient than conventional NAS and can outperform all TC heuristics reported so far. Nevertheless it is still a very time consuming process, finding a good solution in the vast search space of layer-wise TC. We show that, it is possible to reduce the parameter size upto 85% for the cost of 0.1–1% of Top-1 accuracy on our vision processing benchmarks. Further, it is shown that the compressed model occupies just 20% of the original memory size which is required for storing the entire uncompressed model, with an increase in the inference speed of upto 2.5 times without much loss in the performance indicating potential gains for embedded systems.

This publication was created as part of the research project “KI Delta Learning” (project number: 19A19013K) funded by the Federal Ministry for Economic Affairs and Energy (BMWi) on the basis of a decision by the German Bundestag.