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Compact building blocks for artificial neural networks

Compact building blocks for artificial neural networks

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A four quadrant analogue multiplier, an activation function and a differentiator intended for the implementation of backpropagation algorithms are proposed. The priorities for the design are low-power, low-voltage, and minimum silicon area. Breadboard results, showing good agreement with the theoretical ones, are reported.

Inspec keywords: neural nets; backpropagation; analogue multipliers; low-power electronics; differentiating circuits

Other keywords: low-power low-voltage circuit; silicon area; activation function; analogue multiplier; artificial neural network; differentiator; backpropagation algorithm; building block

Subjects: Neural net devices; Neural nets (circuit implementations)

References

    1. 1)
      • B. Gilbert . Translinear circuits: a proposed classification. Electron. Lett. , 1 , 14 - 16
    2. 2)
      • B. Widrow , M.A. Lehr . 30 years of adaptive neural networks: perceptron,madaline, and backpropagation. IEEE Proc. , 9 , 1415 - 1442
    3. 3)
      • B. Gilbert , C. Toumazou , F.J. Lidgey , D.G. Haigh . (1990) Current-mode circuits from a translinear viewpoint: a tutorial, Analogue IC design: the current-mode approach.
    4. 4)
      • S. Haykin . Neural networks: a comprehensive foundation.
    5. 5)
      • Melendez, M., Silva, J.: `Low power/minimum transistor building blocks forthe implementation of backpropagation algorithms', Proc. IEEE Mid. Symp. Circ. Syst., 1998, p. 1334–1337.
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