2 papers
cs.LG2022
Combining Gradients and Probabilities for Heterogeneous Approximation of Neural Networks
Elias Trommer, Bernd Waschneck, Akash Kumar
This work explores the search for heterogeneous approximate multiplier configurations for neural networks that produce high accuracy and low energy consumption. We discuss the vali…
cs.DS2021
dCSR: A Memory-Efficient Sparse Matrix Representation for Parallel Neural Network Inference
Elias Trommer, Bernd Waschneck, Akash Kumar
Reducing the memory footprint of neural networks is a crucial prerequisite for deploying them in small and low-cost embedded devices. Network parameters can often be reduced signif…