1 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators
Charles Hong, Qijing Huang, Grace Dinh +2
In the hardware design space exploration process, it is critical to optimize both hardware parameters and algorithm-to-hardware mappings. Previous work has largely approached this…
hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation
Charles Hong, Brendan Roberts, Huijae An +3
Large language models (LLMs) are playing an increasingly large role in domains such as code generation, including hardware code generation, where Verilog is the key language. Howev…
Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators
Chirag Sakhuja, Charles Hong, Calvin Lin
This paper presents a tool for automatically exploring the design space of deep learning accelerators (DLAs). Our main advancement is Starlight, a data-driven performance model tha…
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…