Name
Abstract | ||
|---|---|---|
The brain-inspired computing, known as neuromorphic computing has demonstrated great potential in revolutionizing computation for high efficiency. In the neuromorphic engine, tremendous computing and power efficiency are achieved on a single chip. However, the development progress is slow in the neuromorphic designs based on conventional nanotechnologies. The occurrence and utilization of memristor technology pushed the development of neuromorphic computing forward into a new era. Matrix-vector multiplication, which is the basic computation in the neural network can be implemented by the memristor crossbar naturally and efficiently. Recently, various neuromorphic systems have been widely developed for cognition and perception applications. In this work, the development status is reviewed from the aspects of device, circuit, system, and algorithm. The challenges and futures are studied. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3109453.3123960 | PROCEEDINGS OF THE 4TH ACM INTERNATIONAL CONFERENCE ON NANOSCALE COMPUTING AND COMMUNICATION (ACM NANOCOM 2017) |
Field | DocType | Citations |
Electrical efficiency,Memristor,Unconventional computing,Computer science,Neuromorphic engineering,Electronic engineering,Chip,Artificial neural network,Stochastic computing,Crossbar switch | Conference | 0 |
PageRank | References | Authors |
0.34 | 22 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenchen Liu | 1 | 90 | 17.45 |
| Fuqiang Liu | 2 | 270 | 24.48 |
| Hai Li | 3 | 2435 | 208.37 |