NDLI: Biologically plausible VLSI neural network implementation with asynchronous neuron and spike-based synapse

Content Provider	IEEE Xplore Digital Library
Author	Il Song Han
Copyright Year	2005
Description	Author affiliation: Dept. of Electron. & Electr. Eng., Sheffield Univ., UK (Il Song Han)
Abstract	This paper describes a new asynchronous spike based neural networks VLSI implementation, inspired by the biological plausibility and low power requirement. The voltage-controlled linear conductance produces the synaptic function of multiplication, weight programming, and summation of synaptic spike currents for the neuron. The operation speed of synaptic computation is up to 300 Mega operations with a small power consumption of 33 microwatts. The overall power consumption can be less in real applications, as individual synapse only consumes the power when there is an active neural input. The neuron is based on multiple combinations of synapses and the HSPICE simulation demonstrates the asynchronous spike behavior of integration-and-firing with a refractory period. The advantages of asynchronous operation, removal of reference clocks, and low voltage operation are exhibited compared to previous pulse-based analogue-mixed neural networks VLSI. The asynchronous spike based neural networks in 0.18/spl mu/m CMOS VLSI technology is proposed to provide the advantage of analogue-mixed neural network VLSI with small power consumption and no need for a synchronous operation.
Starting Page	3244
Ending Page	3248
File Size	2310561
Page Count	5
File Format	PDF
ISBN	0780390482
DOI	10.1109/IJCNN.2005.1556447
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-31
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Very large scale integration Neural networks Neurons Energy consumption CMOS technology Voltage Functional programming Linear programming Biology computing Clocks
Content Type	Text
Resource Type	Article

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