8 citations · 9 across the 3 of their papers we have counts for
8 papers
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…
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…
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin +4
The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs. Recent efforts to r…
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage +9
We present DeepCABAC, a novel context-adaptive binary arithmetic coder for compressing deep neural networks. It quantizes each weight parameter by minimizing a weighted rate-distor…
Robust and Communication-Efficient Federated Learning from Non-IID Data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller +1
Federated Learning allows multiple parties to jointly train a deep learning model on their combined data, without any of the participants having to reveal their local data to a cen…
Entropy-Constrained Training of Deep Neural Networks
Simon Wiedemann, Arturo Marban, Klaus-Robert Müller +1
We propose a general framework for neural network compression that is motivated by the Minimum Description Length (MDL) principle. For that we first derive an expression for the en…