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Found 4 papers, 2 papers with code
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks
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.
Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment
We achieve this for the LLaMA-2 7B model by combining the SparseGPT one-shot pruning method and sparse pretraining of those models on a subset of the SlimPajama dataset mixed with a Python subset of The Stack dataset.
Sparse Fine-tuning for Inference Acceleration of Large Language Models
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.
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models
We perform an in-depth study of the accuracy-compression trade-off for unstructured weight pruning of BERT models.
