Name
Abstract | ||
|---|---|---|
In the post Moore’s era, conventional electronic digital computing platforms have encountered escalating challenges to support massively parallel and energy-hungry artificial intelligence (AI) workloads. Intelligent applications in data centers, edge devices, and autonomous vehicles have restricted requirements in throughput, power, and latency, which raises a high demand for a revolutionary neurocomputing solution. Optical neural network (ONN) is a promising hardware platform that could represent a paradigm shift in efficient neurocomputing with its ultra-fast speed, high parallelism, and low energy consumption. In recent years, efforts have been made to facilitate the ONN design stack and push forward the practical application of optical neural accelerators. In this tutorial, we give an overview of state-of-the-art cross-layer co-design methodologies for scalable, robust, and self-learnable ONN designs across the circuit, architecture, and algorithm levels. Besides, we analyze challenges and highlight emerging directions targeting next-generation optics for AI. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCSII.2022.3171170 | IEEE Transactions on Circuits and Systems II: Express Briefs |
Keywords | DocType | Volume |
Optical neural network,optical computing,scalability,robustness,trainability,software-hardware co-design | Journal | 69 |
Issue | ISSN | Citations |
6 | 1549-7747 | 0 |
PageRank | References | Authors |
0.34 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiaqi Gu | 1 | 18 | 6.97 |
| Chenghao Feng | 2 | 5 | 3.96 |
| Hanqing Zhu | 3 | 0 | 1.35 |
| T. Chen | 4 | 49 | 10.96 |
| David Z. Pan | 5 | 2653 | 237.64 |