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A reconfigurable real‐time neuromorphic hardware for spiking winner‐take‐all network
Author(s) -
Abdoli Behrooz,
Safari Saeed
Publication year - 2020
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.2877
Subject(s) - computer science , field programmable gate array , scalability , neuromorphic engineering , winner take all , computer hardware , pipeline (software) , multiplier (economics) , spiking neural network , multiprocessing , embedded system , artificial neural network , computer architecture , parallel computing , artificial intelligence , database , economics , macroeconomics , programming language
Summary The central nervous system receives a vast amount of sensory inputs, and it should be able to discriminate and recognize different kinds of multisensory information. Winner‐take‐all (WTA) consists of a simple recurrent neural network carrying out discrimination of input signals through competition. This paper presents a real‐time scalable digital hardware implementation of the spiking WTA network. The need for concurrent computing, real‐time performance, proper accuracy, and the reconfigurable device has led to the field‐programmable gate array (FPGA) as the target hardware platform. A set of techniques is employed to lessen memory and resource usage. The proposed architecture consists of multiprocessing elements, which share hardware resources between a specific number of neurons. We introduce a novel connectivity array for neurons (dedicated to the WTA network) to cut down memory usage. Also, a multiplier‐less method in the neuron model and a novel tree adder in the synapse processing unit are designed to improve computational efficiency. The proposed network simulates 4,500 neurons in real time on a Xilinx Artix‐7 FPGA, while a scalable architecture facilitates the implementation of up to 20,000 neurons on this device. The pipeline structure can guarantee real‐time performance for large‐scale networks. Based on simulation and physical synthesis results, the presented network mimics biological WTA dynamics and consumes efficient hardware resources.