1.1k citations · 1.5k across the 16 of their papers we have counts for
4 papers · 1 filter
To 2:4 Sparsity and Beyond: Neuron-level Activation Function to Accelerate LLM Pre-Training
Meghana Madhyastha, Daniel Haziza, Jesse Cai +3
Trainings of Large Language Models are generally bottlenecked by matrix multiplications. In the Transformer architecture, a large portion of these operations happens in the Feed Fo…
Set Block Decoding is a Language Model Inference Accelerator
Itai Gat, Heli Ben-Hamu, Marton Havasi +6
Autoregressive next token prediction language models offer powerful capabilities but face significant challenges in practical deployment due to the high computational and memory co…
Accelerating Transformer Inference and Training with 2:4 Activation Sparsity
Daniel Haziza, Timothy Chou, Dhruv Choudhary +7
In this paper, we demonstrate how to leverage 2:4 sparsity, a popular hardware-accelerated GPU sparsity pattern, to activations to accelerate large language model training and infe…
Characterizing and Efficiently Accelerating Multimodal Generation Model Inference
Yejin Lee, Anna Sun, Basil Hosmer +27
Generative artificial intelligence (AI) technology is revolutionizing the computing industry. Not only its applications have broadened to various sectors but also poses new system…