Name
Title | ||
|---|---|---|
Design and Analysis of Real Time Spiking Neural Network Decoder for Neuromorphic Chips |
Abstract | ||
|---|---|---|
Neuromorphic computing, which is based on non-traditional architectures that mimic bio-neurological process, could offer potentially disruptive capabilities and high energy-efficiency in real-time data monitoring and prediction, and resource allocation. The first step for de-signing neuromorphic chips is to explore effective design methodologies for real time and energy efficient spiking neural network (SNN) encoders and decoders. In this paper, a spike timing dependent plasticity principle (STDP) based decoder is designed and optimized. As shown in our experimental results, the proposed STDP based SNN decoder achieves good performance in information recovery with multiple scales.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3354265.3354280 | Proceedings of the International Conference on Neuromorphic Systems |
Keywords | Field | DocType |
Neuromorphic computing, decoder, encoder | Computer science,Neuromorphic engineering,Control engineering,Computer hardware,Spiking neural network | Conference |
ISBN | Citations | PageRank |
978-1-4503-7680-8 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenyuan Zhao | 1 | 0 | 0.34 |
| Lingjia Liu | 2 | 799 | 92.58 |
| Yi Yang | 3 | 92 | 9.96 |