no code implementations • EMNLP (sustainlp) 2020 • Urmish Thakker, Jesse Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina
We evaluate the impact of this technique on 5 NLP benchmarks across multiple tasks (Translation, Intent Detection, Language Modeling) and show that for similar accuracy values and compression factors, HMF can achieve more than 2. 32x faster inference run-time than pruning and 16. 77% better accuracy than LMF.
no code implementations • 4 Oct 2019 • Urmish Thakker, Igor Fedorov, Jesse Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina
This paper introduces a method to compress RNNs for resource constrained environments using Kronecker product (KP).
no code implementations • 12 Jun 2019 • Urmish Thakker, Jesse Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina
Recurrent neural networks can be large and compute-intensive, yet many applications that benefit from RNNs run on small devices with very limited compute and storage capabilities while still having run-time constraints.
no code implementations • 7 Jun 2019 • Urmish Thakker, Jesse Beu, Dibakar Gope, Chu Zhou, Igor Fedorov, Ganesh Dasika, Matthew Mattina
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.
no code implementations • 4 Mar 2019 • Dibakar Gope, Ganesh Dasika, Matthew Mattina
Machine learning-based applications are increasingly prevalent in IoT devices.
no code implementations • 4 Mar 2019 • Partha Maji, Andrew Mundy, Ganesh Dasika, Jesse Beu, Matthew Mattina, Robert Mullins
The Winograd or Cook-Toom class of algorithms help to reduce the overall compute complexity of many modern deep convolutional neural networks (CNNs).