SHF: Medium: A Neursoymbolic Framework for High-level Synthesis of Multi-Task Learning (Ne — NSF Award to University of California
The growing demand for smart and autonomous systems has driven a surge in the deployment of edge devices. However, the limited computational resources and energy constraints of these devices pose significant challenges for deploying complex deep neural networks (DNNs). Optimizing DNNs for edge devices is crucial to unl
| Award title | SHF: Medium: A Neursoymbolic Framework for High-level Synthesis of Multi-Task Learning (Ne |
|---|---|
| Award ID | 2504809 |
| Awardee | University of California-Irvine |
| City | IRVINE |
| State | CA |
| Amount obligated | $900,000 |
| Principal investigator | Salma Elmalaki |
| Program | Software & Hardware Foundation |
| Start date | 10/01/2025 |
| Abstract | The growing demand for smart and autonomous systems has driven a surge in the deployment of edge devices. However, the limited computational resources and energy constraints of these devices pose significant challenges for deploying complex deep neural networks (DNNs). Optimizing DNNs for edge devices is crucial to unlock their full potential and enable a wider range of innovative applications. This project’s novelties lie in developing a new generation of tools that can automatically generate h |
| Source | NSF Awards |
$799/mo
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