Name
Abstract | ||
|---|---|---|
Editor’s notes: Advanced computing systems have long been enablers for breakthroughs in artificial intelligence (AI) and machine learning (ML) algorithms, either through sheer computational power or form-factor miniaturization. However, as AI/ML algorithms become more complex and the size of data sets increases, existing computing platforms are no longer sufficient to bridge the gap between... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/MDAT.2022.3161126 | IEEE Design & Test |
Keywords | DocType | Volume |
Field programmable gate arrays,Hardware,Machine learning,Computer architecture,Optimization,Neural networks,Graphics processing units | Journal | 39 |
Issue | ISSN | Citations |
3 | 2168-2356 | 1 |
PageRank | References | Authors |
0.39 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sathwika Bavikadi | 1 | 4 | 1.16 |
| Abhijitt Dhavlle | 2 | 6 | 4.18 |
| Amlan Ganguly | 3 | 5 | 3.57 |
| Anand Haridass | 4 | 1 | 0.39 |
| Hagar Hendy | 5 | 1 | 0.39 |
| Cory Merkel | 6 | 8 | 2.03 |
| Vijay Janapa Reddi | 7 | 2931 | 140.26 |
| Purab Ranjan Sutradhar | 8 | 5 | 2.56 |
| Arun Joseph | 9 | 1 | 0.39 |
| Sai Manoj Pudukotai Dinakarrao | 10 | 66 | 18.81 |