11 citations · 13 across the 2 of their papers we have counts for
6 papers
Brevity is the soul of wit: Pruning long files for code generation
Aaditya K. Singh, Yu Yang, Kushal Tirumala +2
Data curation is commonly considered a "secret-sauce" for LLM training, with higher quality data usually leading to better LLM performance. Given the scale of internet-scraped corp…
CHAI: Clustered Head Attention for Efficient LLM Inference
Saurabh Agarwal, Bilge Acun, Basil Hosmer +5
Large Language Models (LLMs) with hundreds of billions of parameters have transformed the field of machine learning. However, serving these models at inference time is both compute…
Large Language Models for Compiler Optimization
Chris Cummins, Volker Seeker, Dejan Grubisic +8
We explore the novel application of Large Language Models to code optimization. We present a 7B-parameter transformer model trained from scratch to optimize LLVM assembly for code…
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
Youwei Liang, Kevin Stone, Ali Shameli +8
Finding the optimal pass sequence of compilation can lead to a significant reduction in program size and/or improvement in program efficiency. Prior works on compilation pass order…
To Filter Prune, or to Layer Prune, That Is The Question
Sara Elkerdawy, Mostafa Elhoushi, Abhineet Singh +2
Recent advances in pruning of neural networks have made it possible to remove a large number of filters or weights without any perceptible drop in accuracy. The number of parameter…
Accelerating Training using Tensor Decomposition
Mostafa Elhoushi, Ye Henry Tian, Zihao Chen +2
Tensor decomposition is one of the well-known approaches to reduce the latency time and number of parameters of a pre-trained model. However, in this paper, we propose an approach…