Name
Abstract | ||
|---|---|---|
The shift of Convolutional Neural Networks (ConvNets) into low-power devices with limited compute and memory resources calls for cross-layer strategies spanning from hardware to software optimization. This work answers to this need, presenting a collection of tools for efficient deployment of ConvNets on the edge. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/VLSI-SOC46417.2020.9344075 | 2020 IFIP/IEEE 28th International Conference on Very Large Scale Integration (VLSI-SOC) |
Keywords | DocType | ISSN |
Artificial Intelligence of Things,Deep Learning,Convolutional Neural Networks,Optimization | Conference | 2324-8432 |
ISBN | Citations | PageRank |
978-1-7281-5410-7 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valentino Peluso | 1 | 6 | 3.45 |
| Enrico Macii | 2 | 2405 | 349.96 |
| Andrea Calimera | 3 | 1 | 1.39 |