• DocumentCode
    821694
  • 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
  • Volume
    14
  • Issue
    5
  • fYear
    2003
  • Firstpage
    1176
  • Lastpage
    1186
  • 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;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2003.816034
  • Filename
    1243719