Name
Abstract | ||
|---|---|---|
A prevalent challenge for Deep Learning (DL) accelerators is how they are programmed to sustain utilization without impacting end-user productivity. Little prior effort has been devoted to the effective management of their on-chip Scratch-Pad Memory (SPM) across the DL operations of a Deep Neural Network (DNN). This is especially critical due to trends in complex network topologies and the emergen... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISPASS51385.2021.00046 | 2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) |
Keywords | DocType | ISBN |
Deep learning,Productivity,Runtime,Memory management,Market research,Data structures,Software | Conference | 978-1-7281-8643-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| subhankar pal | 1 | 32 | 5.27 |
| Swagath Venkataramani | 2 | 631 | 39.33 |
| Vijayalakshmi Srinivasan | 3 | 1077 | 83.50 |
| Kailash Gopalakrishnan | 4 | 361 | 29.76 |