Michael Goin — accepted AI-conference papers

2022

The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models

Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran +2

EMNLP 2022main

In this paper, we consider the problem of sparsifying BERT models, which are a key building block for natural language processing, in order to reduce their storage and computational cost. We introduce the Optimal BERT Surgeon (oBERT), an efficient and accurate pruning method based on approximate sec…

Cited by 146 Source ↗PDF ↗code ↗Scholar ↗

2020

Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks

Mark Kurtz, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin +4

ICML 2020poster

Optimizing convolutional neural networks for fast inference has recently become an extremely active area of research. One of the go-to solutions in this context is weight pruning, which aims to reduce computational and memory footprint by removing large subsets of the connections in a neural network…

Cited by 191 Source ↗PDF ↗Scholar ↗