Compressing RNNs for IoT devices by 15-38x using Kronecker Products

• Published in: arXiv.org 2020-01
• Main Authors: Thakker, Urmish, Beu, Jesse, Gope, Dibakar, Chu, Zhou, Fedorov, Igor, Dasika, Ganesh, Mattina, Matthew
• Format: Article
• Language: English
• Subjects: Accuracy; Benchmarks; Compressing; Counting; Neural networks; Recurrent neural networks; Video compression
• ISSN: 2331-8422

Recurrent Neural Networks (RNN) can be difficult to deploy on resource constrained devices due to their size.As a result, there is a need for compression techniques that can significantly compress RNNs without negatively impacting task accuracy. This paper introduces a method to compress RNNs for resource constrained environments using Kronecker product (KP). KPs can compress RNN layers by 15-38x with minimal accuracy loss. By quantizing the resulting models to 8-bits, we further push the compression factor to 50x. We show that KP can beat the task accuracy achieved by other state-of-the-art compression techniques across 5 benchmarks spanning 3 different applications, while simultaneously improving inference run-time. We show that the KP compression mechanism does introduce an accuracy loss, which can be mitigated by a proposed hybrid KP (HKP) approach. Our HKP algorithm provides fine-grained control over the compression ratio, enabling us to regain accuracy lost during compression by adding a small number of model parameters.