EAGER: Accelerating Scalable Stochastic Neuro-Inspired Computing With Spintronics: Devices — NSF Award to Iowa State University (I
Remarkable advances in Artificial Intelligence (AI) have demonstrated near-human cognitive performance in various applications. However, state-of-the-art AI still exhibits a large (orders of magnitude) efficiency gap compared to human brains. Enabling efficient AI hardware/software systems will be the key to deploying
| Award title | EAGER: Accelerating Scalable Stochastic Neuro-Inspired Computing With Spintronics: Devices |
|---|---|
| Award ID | 2534279 |
| Awardee | Iowa State University |
| City | AMES |
| State | IA |
| Amount obligated | $238,794 |
| Principal investigator | Cheng Wang |
| Program | FET-Fndtns of Emerging Tech |
| Start date | 09/01/2025 |
| Abstract | Remarkable advances in Artificial Intelligence (AI) have demonstrated near-human cognitive performance in various applications. However, state-of-the-art AI still exhibits a large (orders of magnitude) efficiency gap compared to human brains. Enabling efficient AI hardware/software systems will be the key to deploying AI in various domains, including transportation, healthcare, and defense. Taking cues from the biological brains, neuro-inspired computing recently emerges as a promising approach |
| Source | NSF Awards |
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