5 papers
ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling
Heng Ping, Arijit Bhattacharjee, Peiyu Zhang +5
Mixture-of-Agents (MoA) architectures improve inference-time scaling by organizing multiple LLM agents into layered reasoning pipelines. However, existing MoA variants fail to sust…
VeriMoA: A Mixture-of-Agents Framework for Spec-to-HDL Generation
Heng Ping, Arijit Bhattacharjee, Peiyu Zhang +8
Automation of Register Transfer Level (RTL) design can help developers meet increasing computational demands. Large Language Models (LLMs) show promise for Hardware Description Lan…
OptiML: An End-to-End Framework for Program Synthesis and CUDA Kernel Optimization
Arijit Bhattacharjee, Heng Ping, Son Vu Le +3
Generating high-performance CUDA kernels remains challenging due to the need to navigate a combinatorial space of low-level transformations under noisy and expensive hardware feedb…
OMPILOT: Harnessing Transformer Models for Auto Parallelization to Shared Memory Computing Paradigms
Arijit Bhattacharjee, Ali TehraniJamsaz, Le Chen +4
Recent advances in large language models (LLMs) have significantly accelerated progress in code translation, enabling more accurate and efficient transformation across programming…
CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming
Ali TehraniJamsaz, Arijit Bhattacharjee, Le Chen +3
Recent advancements in Large Language Models (LLMs) have renewed interest in automatic programming language translation. Encoder-decoder transformer models, in particular, have sho…