5 citations · 5 across the 4 of their papers we have counts for
5 papers
Playing Psychic: Using Thought Trees to Predict Reasoning Models Accuracy on Coding Tasks
Jiaxin Fang, Runyuan He, Sahil Bhatia +2
Recent advances in large language models (LLMs) have shown that test-time scaling can substantially improve model performance on complex tasks, particularly in the coding domain. U…
Autocomp: A Powerful and Portable Code Optimizer for Tensor Accelerators
Charles Hong, Sahil Bhatia, Alvin Cheung +1
Hardware accelerators, especially those designed for tensor processing, have become ubiquitous in today's computing landscape. However, even with significant efforts in building co…
LLM-Aided Compilation for Tensor Accelerators
Charles Hong, Sahil Bhatia, Altan Haan +4
Hardware accelerators, in particular accelerators for tensor processing, have many potential application domains. However, they currently lack the software infrastructure to suppor…
Verified Code Transpilation with LLMs
Sahil Bhatia, Jie Qiu, Niranjan Hasabnis +2
Domain-specific languages (DSLs) are integral to various software workflows. Such languages offer domain-specific optimizations and abstractions that improve code readability and m…
Tenspiler: A Verified Lifting-Based Compiler for Tensor Operations (Extended Version)
Jie Qiu, Colin Cai, Sahil Bhatia +3
Tensor processing infrastructures such as deep learning frameworks and specialized hardware accelerators have revolutionized how computationally intensive code from domains such as…