15 citations · 19 across the 5 of their papers we have counts for
6 papers
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering
Thorir Mar Ingolfsson, Mark Vero, Xiaying Wang +3
The computational demands of neural architecture search (NAS) algorithms are usually directly proportional to the size of their target search spaces. Thus, limiting the search to h…
SNE: an Energy-Proportional Digital Accelerator for Sparse Event-Based Convolutions
Alfio Di Mauro, Arpan Suravi Prasad, Zhikai Huang +3
Event-based sensors are drawing increasing attention due to their high temporal resolution, low power consumption, and low bandwidth. To efficiently extract semantically meaningful…
Training Quantised Neural Networks with STE Variants: the Additive Noise Annealing Algorithm
Matteo Spallanzani, Gian Paolo Leonardi, Luca Benini
Training quantised neural networks (QNNs) is a non-differentiable optimisation problem since weights and features are output by piecewise constant functions. The standard solution…
Proceedings of the DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous Architectures (SLOHA 2021)
Frank Hannig, Paolo Meloni, Matteo Spallanzani +1
This volume contains the papers accepted at the first DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous Architectures (SLOHA 2021), held virtua…
Analytical aspects of non-differentiable neural networks
Gian Paolo Leonardi, Matteo Spallanzani
Research in computational deep learning has directed considerable efforts towards hardware-oriented optimisations for deep neural networks, via the simplification of the activation…
Additive Noise Annealing and Approximation Properties of Quantized Neural Networks
Matteo Spallanzani, Lukas Cavigelli, Gian Paolo Leonardi +2
We present a theoretical and experimental investigation of the quantization problem for artificial neural networks. We provide a mathematical definition of quantized neural network…