Name
Abstract | ||
|---|---|---|
There has been a lot of recent interest in applying machine learning (ML) to the design of systems, which purports to aid human experts in extracting new insights leading to better systems. In this work, we share our experiences with applying ML to improve one aspect of networks-on-chips (NoC) to uncover new ideas and approaches, which eventually led us to a new arbitration scheme that is effective for NoCs under heavy contention. However, a significant amount of human effort and creativity was still needed to optimize just one aspect (arbitration) of what is only one component (the NoC) of the overall processor. This leads us to conclude that much work (and opportunity!) remains to be done in the area of ML-driven architecture design. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/HPCA47549.2020.00058 | 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA) |
Keywords | DocType | ISSN |
NoC case study,machine learning,human experts,networks-on-chips,arbitration scheme,heavy contention,ML-driven architecture design | Conference | 1530-0897 |
ISBN | Citations | PageRank |
978-1-7281-6150-1 | 1 | 0.36 |
References | Authors | |
18 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jieming Yin | 1 | 43 | 4.72 |
| Subhash Sethumurugan | 2 | 1 | 0.36 |
| Yasuko Eckert | 3 | 33 | 4.60 |
| Chintan Patel | 4 | 1 | 0.36 |
| Alan Smith | 5 | 1 | 0.36 |
| Eric Morton | 6 | 1 | 0.36 |
| Mark Oskin | 7 | 906 | 76.63 |
| Natalie Enright Jerger | 8 | 1268 | 56.57 |
| Gabriel H. Loh | 9 | 12 | 1.85 |