Hardware Neural Network for a Visual Inspection System
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
International Journal of Reconfigurable Computing - Selected papers from ReCoSoc08
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Visual processing platform based on artificial retinas
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Evolvable block-based neural network design for applications in dynamic environments
VLSI Design - Special issue on selected papers from the midwest symposium on circuits and systems
Hardware spiking neural network prototyping and application
Genetic Programming and Evolvable Machines
ACM SIGARCH Computer Architecture News
Modular Neural Tile Architecture for Compact Embedded Hardware Spiking Neural Network
Neural Processing Letters
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A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as input-driven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing software-based event-driven approaches. As an alternative we have designed an efficient time-based computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important.