16 citations · 31 across the 4 of their papers we have counts for
9 papers
Restructurable Activation Networks
Kartikeya Bhardwaj, James Ward, Caleb Tung +6
Is it possible to restructure the non-linear activation functions in a deep network to create hardware-efficient models? To address this question, we propose a new paradigm called…
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…
High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands
Dibakar Gope, Jesse Beu, Matthew Mattina
Matrix multiplications between asymmetric bit-width operands, especially between 8- and 4-bit operands are likely to become a fundamental kernel of many important workloads includi…
The gem5 Simulator: Version 20.0+
Jason Lowe-Power, Abdul Mutaal Ahmad, Ayaz Akram +75
The open-source and community-supported gem5 simulator is one of the most popular tools for computer architecture research. This simulation infrastructure allows researchers to mod…
Ternary MobileNets via Per-Layer Hybrid Filter Banks
Dibakar Gope, Jesse Beu, Urmish Thakker +1
MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years. New applica…
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…