A low-voltage, low-power reconfigurable current-mode softmax circuit for analog neural networks


Abstract:

This paper presents a novel low-power low-voltage analog implementation of the softmax function, with electrically adjustable amplitude and slope parameters. We propose a modular design, which can be scaled by the number of inputs (and of corresponding outputs). It is composed of input current–voltage linear converter stages (1st stages), MOSFETs operating in a subthreshold regime implementing the exponential functions (2nd stages), and analog divider stages (3rd stages). Each stage is only composed of p-type MOSFET transistors. Designed in a 0.18 µm CMOS technology (TSMC), the proposed softmax circuit can be operated at a supply voltage of 500 mV. A ten-input/ten-output realization occupies a chip area of 2570 µm2 and consumes only 3 µW of power, representing a very compact and energy-efficient option compared to the corresponding digital implementations.

Año de publicación:

2021

Keywords:

  • Machine learning
  • Deep Neural Networks
  • Softmax
  • Activation functions

Fuente:

scopusscopus

Tipo de documento:

Article

Estado:

Acceso abierto

Áreas de conocimiento:

  • Red neuronal artificial
  • Ingeniería electrónica
  • Ingeniería electrónica

Áreas temáticas:

  • Ciencias de la computación
  • Física aplicada