898 citations · 2.7k across the 20 of their papers we have counts for
22 papers
EfQAT: An Efficient Framework for Quantization-Aware Training
Saleh Ashkboos, Bram Verhoef, Torsten Hoefler +2
Quantization-aware training (QAT) schemes have been shown to achieve near-full precision accuracy. They accomplish this by training a quantized model for multiple epochs. This is c…
Differentiable Transportation Pruning
Yunqiang Li, Jan C. van Gemert, Torsten Hoefler +3
Deep learning algorithms are increasingly employed at the edge. However, edge devices are resource constrained and thus require efficient deployment of deep neural networks. Prunin…
A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
Manuel Le Gallo, Riduan Khaddam-Aljameh, Milos Stanisavljevic +26
The need to repeatedly shuttle around synaptic weight values from memory to processing units has been a key source of energy inefficiency associated with hardware implementation of…
On the visual analytic intelligence of neural networks
Stanisław Woźniak, Hlynur Jónsson, Giovanni Cherubini +2
Visual oddity task was conceived as a universal ethnic-independent analytic intelligence test for humans. Advancements in artificial intelligence led to important breakthroughs, ye…
Towards efficient end-to-end speech recognition with biologically-inspired neural networks
Thomas Bohnstingl, Ayush Garg, Stanisław Woźniak +3
Automatic speech recognition (ASR) is a capability which enables a program to process human speech into a written form. Recent developments in artificial intelligence (AI) have led…
Learning in Deep Neural Networks Using a Biologically Inspired Optimizer
Giorgia Dellaferrera, Stanislaw Wozniak, Giacomo Indiveri +2
Plasticity circuits in the brain are known to be influenced by the distribution of the synaptic weights through the mechanisms of synaptic integration and local regulation of synap…