Name
Title | ||
|---|---|---|
XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V Based IoT End Nodes |
Abstract | ||
|---|---|---|
Heavily quantized fixed-point arithmetic is becoming a common approach to deploy Convolutional Neural Networks (CNNs) on limited-memory low-power IoT end-nodes. However, this trend is narrowed by the lack of support for low-bitwidth in the arithmetic units of state-of-the-art embedded Microcontrollers (MCUs). This work proposes a multi-precision arithmetic unit fully integrated into a RISC-V proce... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TETC.2021.3072337 | IEEE Transactions on Emerging Topics in Computing |
Keywords | DocType | Volume |
Internet of Things,Field programmable gate arrays,Neural networks,Hardware,Quantization (signal),Microcontrollers,Kernel | Journal | 9 |
Issue | ISSN | Citations |
3 | 2168-6750 | 1 |
PageRank | References | Authors |
0.37 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Angelo Garofalo | 1 | 4 | 2.47 |
| Giuseppe Tagliavini | 2 | 21 | 3.91 |
| Francesco Conti 0001 | 3 | 125 | 18.24 |
| Luca Benini | 4 | 13116 | 1188.49 |
| Davide Rossi | 5 | 416 | 47.47 |