267 citations · 399 across the 9 of their papers we have counts for
3 papers · 1 filter
FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4bit-Compact Multilayer Perceptrons
Simon Wiedemann, Suhas Shivapakash, Pablo Wiedemann +4
With the growing demand for deploying deep learning models to the "edge", it is paramount to develop techniques that allow to execute state-of-the-art models within very tight and…
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)
Arturo Marban, Daniel Becking, Simon Wiedemann +1
Deep neural networks (DNN) have shown remarkable success in a variety of machine learning applications. The capacity of these models (i.e., number of parameters), endows them with…
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training
Simon Wiedemann, Temesgen Mehari, Kevin Kepp +1
Deep Neural Networks are successful but highly computationally expensive learning systems. One of the main sources of time and energy drains is the well known backpropagation (back…