1 code implementation • 28 Jun 2022 • Vitaliy Chiley, Vithursan Thangarasa, Abhay Gupta, Anshul Samar, Joel Hestness, Dennis Decoste
However, training them requires substantial accelerator memory for saving large, multi-resolution activations.
Ranked #313 on Image Classification on ImageNet (using extra training data)
no code implementations • 19 Apr 2021 • Mihir Pendse, Vithursan Thangarasa, Vitaliy Chiley, Ryan Holmdahl, Joel Hestness, Dennis Decoste
The inverted residual bottleneck block uses lightweight depthwise separable convolutions to reduce computation by decomposing convolutions into a pointwise convolution and a depthwise convolution.
no code implementations • 2 Jul 2020 • Abhinav Venigalla, Atli Kosson, Vitaliy Chiley, Urs Köster
Neural network training is commonly accelerated by using multiple synchronized workers to compute gradient updates in parallel.
no code implementations • 25 Mar 2020 • Atli Kosson, Vitaliy Chiley, Abhinav Venigalla, Joel Hestness, Urs Köster
New hardware can substantially increase the speed and efficiency of deep neural network training.
1 code implementation • NeurIPS 2019 • Vitaliy Chiley, Ilya Sharapov, Atli Kosson, Urs Koster, Ryan Reece, Sofia Samaniego de la Fuente, Vishal Subbiah, Michael James
Online Normalization is a new technique for normalizing the hidden activations of a neural network.