The last 50 years has witnessed considerable research in the area of neural networks resulting in a range of architectures, learning algorithms and demonstrative applications. A more recent research trend has focused on the biological plausibility of such networks as a closer abstraction to real neurons may offer improved performance in an adaptable, real-time environment. This poses considerable challenges for engineers particularly in terms of the requirement to realise a low-cost embedded solution. Programmable hardware has been widely recognised as an ideal platform for the adaptable requirements of neural networks and there has been considerable research reported in the literature. This paper aims to review this body of research to identify the key lessons learned and, in particular, to identify the remaining challenges for large-scale implementations of spiking neural networks on FPGAs.
- Field programmable gate arrays (FPGAs)
- Hardware implementation
- Spiking neural network (SNN)