SHF: Small: Cross-Layer Design Automation for In-Memory Analog Computing — NSF Award to University of South Carolina at Columbia (
Machine learning (ML) has become ubiquitous and interwoven into many applications that are important to our daily lives, societal prosperity, and technological progress. However, the large data centers that serve ML workloads are facing tremendous challenges in keeping pace with demand. Additionally, the surge in ML wo
| Award title | SHF: Small: Cross-Layer Design Automation for In-Memory Analog Computing |
|---|---|
| Award ID | 2409697 |
| Awardee | University of South Carolina at Columbia |
| City | COLUMBIA |
| State | SC |
| Amount obligated | $588,801 |
| Principal investigator | Ramtin Zand |
| Program | Software & Hardware Foundation |
| Start date | 06/01/2024 |
| Abstract | Machine learning (ML) has become ubiquitous and interwoven into many applications that are important to our daily lives, societal prosperity, and technological progress. However, the large data centers that serve ML workloads are facing tremendous challenges in keeping pace with demand. Additionally, the surge in ML workload demands has positioned data centers as major contributors to annual energy consumption. This project’s goal is to develop a transformative technology to substantially improv |
| Source | NSF Awards |
$799/mo
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