EMBRACE: emulating biologically-inspired architectures on hardware
NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
A Hardware Accelerated Simulation Environment for Spiking Neural Networks
ARC '09 Proceedings of the 5th International Workshop on Reconfigurable Computing: Architectures, Tools and Applications
International Journal of Reconfigurable Computing - Selected papers from ReCoSoc08
BRAHMS: Novel middleware for integrated systems computation
Advanced Engineering Informatics
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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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.