FuSe2 Topic 1: Energy-efficient, Near-Memory CMOS+X Architecture for Hardware Acceleration — NSF Award to Arizona State University
Deep neural networks (DNNs) have been successfully applied in many domains, including image classification, language models, speech analysis, autonomous vehicles, wireless communications, bioinformatics, and others. Their success stems from their ability to handle vast amounts of data and infer patterns without making
| Award title | FuSe2 Topic 1: Energy-efficient, Near-Memory CMOS+X Architecture for Hardware Acceleration |
|---|---|
| Award ID | 2425535 |
| Awardee | Arizona State University |
| City | SCOTTSDALE |
| State | AZ |
| Amount obligated | $1,306,264 |
| Principal investigator | Sarma Vrudhula |
| Program | FuSe-Future of Semiconductors, NSF-Samsung Partnership |
| Start date | 10/01/2024 |
| Abstract | Deep neural networks (DNNs) have been successfully applied in many domains, including image classification, language models, speech analysis, autonomous vehicles, wireless communications, bioinformatics, and others. Their success stems from their ability to handle vast amounts of data and infer patterns without making assumptions on the underlying dynamics that produced the data. Cloud providers operate large data centers with high-speed computers that continuously perform DNN computations, with |
| Source | NSF Awards |
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