8 citations · 9 across the 2 of their papers we have counts for
2 papers
cs.AR2024★ 1 cited
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…
cs.LG2020★ 8 cited
Learned Hardware/Software Co-Design of Neural Accelerators
Zhan Shi, Chirag Sakhuja, Milad Hashemi +2
The use of deep learning has grown at an exponential rate, giving rise to numerous specialized hardware and software systems for deep learning. Because the design space of deep lea…