220 citations · 405 across the 3 of their papers we have counts for
4 papers
Accelerating Sparse Deep Neural Networks
Asit Mishra, Jorge Albericio Latorre, Jeff Pool +5
As neural network model sizes have dramatically increased, so has the interest in various techniques to reduce their parameter counts and accelerate their execution. An active area…
Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation
Hao Wu, Patrick Judd, Xiaojie Zhang +2
Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by taking advantage of high throughput integer instructions. In thi…
MLPerf Training Benchmark
Peter Mattson, Christine Cheng, Cody Coleman +34
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But M…
Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq
Oleksii Kuchaiev, Boris Ginsburg, Igor Gitman +5
We present OpenSeq2Seq - a TensorFlow-based toolkit for training sequence-to-sequence models that features distributed and mixed-precision training. Benchmarks on machine translati…