Name
Title | ||
|---|---|---|
Implementing Spiking Neural Networks for Real-Time Signal-Processing and Control Applications: A Model-Validated FPGA Approach |
Abstract | ||
|---|---|---|
In this paper, we present two versions of a hardware processing architecture for modeling large networks of leaky-integrate-and-flre (LIF) neurons; the second version provides performance enhancing features relative to the first. Both versions of the architecture use fixed-point arithmetic and have been implemented using a single field-programmable gate array (FPGA). They have successfully simulated networks of over 1000 neurons configured using biologically plausible models of mammalian neural systems. The neuroprocessor has been designed to be employed primarily for use on mobile robotic vehicles, allowing bio-inspired neural processing models to be integrated directly into real-world control environments. When a neuroprocessor has been designed to act as part of the closed-loop system of a feedback controller, it is imperative to maintain strict real-time performance at all times, in order to maintain integrity of the control system. This resulted in the reevaluation of some of the architectural features of existing hardware for biologically plausible neural networks (NNs). In addition, we describe a development system for rapidly porting an underlying model (based on floating-point arithmetic) to the fixed-point representation of the FPGA-based neuroprocessor, thereby allowing validation of the hardware architecture. The developmental system environment facilitates the cooperation of computational neuroscientists and engineers working on embodied (robotic) systems with neural controllers, as demonstrated by our own experience on the Whiskerbot project, in which we developed models of the rodent whisker sensory system. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TNN.2007.891203 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
fixed point arithmetic,field programmable gate array,robotics,real time,mobile robot,control system,process model,mobile robots,neural network,signal processing,feedback,sensory system,field programmable gate arrays,computational modeling,real time systems,fixed point,hardware architecture,model validation,spiking neural network,spiking neural networks,floating point arithmetic | Signal processing,Fixed-point arithmetic,Computer science,Network architecture,Field-programmable gate array,Artificial intelligence,Control system,Spiking neural network,Artificial neural network,Machine learning,Hardware architecture | Journal |
Volume | Issue | ISSN |
18 | 5 | 1045-9227 |
Citations | PageRank | References |
49 | 2.60 | 17 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin J. Pearson | 1 | 215 | 26.34 |
| A. G. Pipe | 2 | 90 | 6.73 |
| Benjamin Mitchinson | 3 | 86 | 7.90 |
| K. Gurney | 4 | 52 | 3.34 |
| Chris Melhuish | 5 | 49 | 2.60 |
| Ian Gilhespy | 6 | 75 | 10.01 |
| Mokhtar Nibouche | 7 | 54 | 3.39 |