Name
Title | ||
|---|---|---|
Configurable Deep Learning Accelerator with Bitwise-accurate Training and Verification |
Abstract | ||
|---|---|---|
This paper introduces an end-to-end solution to a deep neural network (DNN) inference system. We customize and enrich a family of deep-learning accelerators (DLA) based on the NVIDIA open-source deep-learning accelerator (NVDLA). Our exclusive enhancement includes hardware and software parts. The hardware part is the shared multiplier array of both high-efficient regular and depth-wise convolution... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/VLSI-DAT54769.2022.9768062 | 2022 International Symposium on VLSI Design, Automation and Test (VLSI-DAT) |
Keywords | DocType | ISBN |
Deep learning,Training,Power demand,Neural networks,Prototypes,Object detection,Very large scale integration | Conference | 978-1-6654-0921-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shien-Chun Luo | 1 | 0 | 0.34 |
| Kuo-Chiang Chang | 2 | 0 | 0.34 |
| Po-Wei Chen | 3 | 0 | 0.34 |
| Zhao-Hong Chen | 4 | 0 | 0.34 |