30 citations · 78 across the 5 of their papers we have counts for
7 papers
Rank and run-time aware compression of NLP Applications
Urmish Thakker, Jesse Beu, Dibakar Gope +2
Sequence model based NLP applications can be large. Yet, many applications that benefit from them run on small devices with very limited compute and storage capabilities, while sti…
Pushing the limits of RNN Compression
Urmish Thakker, Igor Fedorov, Jesse Beu +4
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 signi…
Run-Time Efficient RNN Compression for Inference on Edge Devices
Urmish Thakker, Jesse Beu, Dibakar Gope +2
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 w…
Compressing RNNs for IoT devices by 15-38x using Kronecker Products
Urmish Thakker, Jesse Beu, Dibakar Gope +4
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 signif…
Measuring scheduling efficiency of RNNs for NLP applications
Urmish Thakker, Ganesh Dasika, Jesse Beu +1
Recurrent neural networks (RNNs) have shown state of the art results for speech recognition, natural language processing, image captioning and video summarizing applications. Many…
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications
Dibakar Gope, Ganesh Dasika, Matthew Mattina
Machine learning-based applications are increasingly prevalent in IoT devices. The power and storage constraints of these devices make it particularly challenging to run modern neu…
Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs
Partha Maji, Andrew Mundy, Ganesh Dasika +3
The Winograd or Cook-Toom class of algorithms help to reduce the overall compute complexity of many modern deep convolutional neural networks (CNNs). Although there has been a lot…