Title :
A new wide range Euclidean distance circuit for neural network hardware implementations
Author :
Gopalan, Anand ; Titus, Albert H.
Author_Institution :
Dept. of Electr. Eng., State Univ. of New York, Buffalo, NY, USA
Abstract :
In this paper, we describe an analog very large-scale integration (VLSI) implementation of a wide range Euclidean distance computation circuit - the key element of many synapse circuits. This circuit is essentially a wide-range absolute value circuit that is designed to be as small as possible (80 × 76 μm) in order to achieve maximum synapse density while maintaining a wide range of operation (0.5 to 4.5 V) and low power consumption (less than 200 μW). The circuit has been fabricated in 1.5-μm technology through MOSIS. We present simulated and experimental results of the circuit, and compare these results. Ultimately, this circuit is intended for use as part of a high-density hardware implementation of a self-organizing map (SOM). We describe how this circuit can be used as part of the SOM and how the SOM is going to be used as part of a larger bio-inspired vision system based on the octopus visual system.
Keywords :
MOS analogue integrated circuits; VLSI; computer vision; neural chips; self-organising feature maps; MOSIS; absolute value circuit; analog VLSI; biologically inspired vision system; computer vision; hardware synapse; neural network; self-organizing map; wide-range Euclidean distance circuit; Analog computers; Artificial neural networks; Circuit simulation; Computer networks; Euclidean distance; Large scale integration; Neural network hardware; Neural networks; Very large scale integration; Voltage;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2003.816034
