POSE: Phase I: Toward a Comprehensive and Extensible Infrastructure for Machine Learning-D — NSF Award to Georgia Tech Research Co
High-Level Synthesis (HLS) is a design methodology that allows developers to create hardware systems, such as accelerators for artificial intelligence and data processing, using common programming languages. HLS improves productivity and accessibility, but fragmented tools, inconsistent evaluation practices, and the ab
| Award title | POSE: Phase I: Toward a Comprehensive and Extensible Infrastructure for Machine Learning-D |
|---|---|
| Award ID | 2549745 |
| Awardee | Georgia Tech Research Corporation |
| City | ATLANTA |
| State | GA |
| Amount obligated | $300,000 |
| Principal investigator | Cong Hao |
| Program | POSE |
| Start date | 04/01/2026 |
| Abstract | High-Level Synthesis (HLS) is a design methodology that allows developers to create hardware systems, such as accelerators for artificial intelligence and data processing, using common programming languages. HLS improves productivity and accessibility, but fragmented tools, inconsistent evaluation practices, and the absence of shared, reproducible datasets hinder progress in HLS research and education. This project addresses these challenges by creating an open-source ecosystem that unifies tool |
| Source | NSF Awards |
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