Collaborative Research: SHF: Medium: Memory-efficient Algorithm and Hardware Co-Design for — NSF Award to University of Southern C
In today's rapidly advancing world of Artificial Intelligence (AI), energy efficiency has emerged as a crucial factor to facilitate the ubiquitous development of intelligent systems. The efficient deployment of AI holds the key to overcoming limitations posed by power-constrained devices and contributes to sustainable
| Award title | Collaborative Research: SHF: Medium: Memory-efficient Algorithm and Hardware Co-Design for |
|---|---|
| Award ID | 2607757 |
| Awardee | University of Southern California |
| City | LOS ANGELES |
| State | CA |
| Amount obligated | $411,682 |
| Principal investigator | Priyadarshini Panda |
| Program | Software & Hardware Foundation, FET-Fndtns of Emerging Tech |
| Start date | 10/01/2025 |
| Abstract | In today's rapidly advancing world of Artificial Intelligence (AI), energy efficiency has emerged as a crucial factor to facilitate the ubiquitous development of intelligent systems. The efficient deployment of AI holds the key to overcoming limitations posed by power-constrained devices and contributes to sustainable technological progress. Neuromorphic computing offers a brain-inspired paradigm of AI, called Spiking Neural Networks (SNNs), that represents a promising step forward in sustainabl |
| Source | NSF Awards |
$799/mo
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