Name
Abstract | ||
|---|---|---|
We investigate Edge-AI Inference (EAI) architectures based on 22nm FD-SOI embedded-MRAM (eMRAM) using quantized neural networks (QNN) for inference applications in harsh industrial conditions having strong magnetic field and wide operating temperature (-40∼125 °C). We achieved best case test accuracy of 98.99% with Quantized-Convolutional Neural Network (QCNN) and 89.94% with Quantized-Multi-layer... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/AICAS51828.2021.9458528 | 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS) |
Keywords | DocType | ISBN |
Service robots,Wearable computers,Conferences,Neural networks,Silicon-on-insulator,Programming,Magnetic fields | Conference | 978-1-6654-1913-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| vivek parmar | 1 | 8 | 5.42 |
| Manan Suri | 2 | 0 | 2.03 |
| Kazutaka Yamane | 3 | 0 | 0.34 |
| Taeyoung Lee | 4 | 0 | 0.34 |
| Nyuk Leong Chung | 5 | 0 | 0.34 |
| Vinayak Naik | 6 | 2 | 3.10 |