Search Results for author: Michael Goin

Found 3 papers, 2 papers with code

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

no code implementations ICML 2020 Mark Kurtz, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, Sage Moore, Nir Shavit, Dan Alistarh

In this paper, we present an in-depth analysis of methods for maximizing the sparsity of the activations in a trained neural network, and show that, when coupled with an efficient sparse-input convolution algorithm, we can leverage this sparsity for significant performance gains.

Image Classification

Sparse Fine-tuning for Inference Acceleration of Large Language Models

2 code implementations10 Oct 2023 Eldar Kurtic, Denis Kuznedelev, Elias Frantar, Michael Goin, Dan Alistarh

While the standard approach is to leverage sparsity for computational reduction, we observe that in the case of memory-bound LLMs sparsity can also be leveraged for reducing memory bandwidth.

Quantization Text Generation +1

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