15 citations · 17 across the 8 of their papers we have counts for
3 papers · 1 filter
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…
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…