114 citations · 316 across the 11 of their papers we have counts for
22 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…
Federated Learning Based on Dynamic Regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro +3
We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen device…
Fast and Accurate: Video Enhancement using Sparse Depth
Yu Feng, Patrick Hansen, Paul N. Whatmough +2
This paper presents a general framework to build fast and accurate algorithms for video enhancement tasks such as super-resolution, deblurring, and denoising. Essential to our fram…
Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices
Urmish Thakker, Paul N. Whatmough, Zhigang Liu +2
Structured matrices, such as those derived from Kronecker products (KP), are effective at compressing neural networks, but can lead to unacceptable accuracy loss when applied to la…
AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs
Srivatsan Krishnan, Zishen Wan, Kshitij Bhardwaj +6
Building domain-specific accelerators for autonomous unmanned aerial vehicles (UAVs) is challenging due to a lack of systematic methodology for designing onboard compute. Balancing…
Information contraction in noisy binary neural networks and its implications
Chuteng Zhou, Quntao Zhuang, Matthew Mattina +1
Neural networks have gained importance as the machine learning models that achieve state-of-the-art performance on large-scale image classification, object detection and natural la…