Name
Title | ||
|---|---|---|
XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V based IoT End Nodes |
Abstract | ||
|---|---|---|
Published in "IEEE Transactions on Emerging Topics in Computing, Volume: 9, Issue: 3, JulySeptember 2021" and orally presented at ARITH 2021. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ARITH51176.2021.00020 | 2021 IEEE 28th Symposium on Computer Arithmetic (ARITH) |
DocType | ISSN | ISBN |
Conference | 1063-6889 | 978-1-6654-2293-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Angelo Garofalo | 1 | 4 | 2.47 |
| Giuseppe Tagliavini | 2 | 0 | 0.34 |
| Francesco Conti | 3 | 2 | 2.48 |
| Luca Benini | 4 | 13116 | 1188.49 |
| Davide Rossi | 5 | 0 | 0.34 |