2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.PL2026
Triton for MTIA: Bridging the Programming Model Gaps for Custom AI Accelerators
Haishan Zhu, Domi Yan, Michael Levesque-Dion +40
The rapid growth in machine learning workloads has fueled the proliferation of custom accelerator architectures. Designed from the ground up, these accelerators often expose progra…
cs.CR2024
Palermo: Improving the Performance of Oblivious Memory using Protocol-Hardware Co-Design
Haojie Ye, Yuchen Xia, Yuhan Chen +6
Oblivious RAM (ORAM) hides the memory access patterns, enhancing data privacy by preventing attackers from discovering sensitive information based on the sequence of memory accesse…
cs.CL2024★ 2 cited
Understanding the Performance and Estimating the Cost of LLM Fine-Tuning
Yuchen Xia, Jiho Kim, Yuhan Chen +4
Due to the cost-prohibitive nature of training Large Language Models (LLMs), fine-tuning has emerged as an attractive alternative for specializing LLMs for specific tasks using lim…