1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…
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…
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…