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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

Shruti R Kulkarni¹, Bipin Rajendran¹

¹Department of Electrical and Computer Engineering, New Jersey Institute of Technology, NJ, 07102, USA.

Neural Networks : the Official Journal of the International Neural Network Society

|April 21, 2018

Summary

This study introduces a Spiking Neural Network (SNN) for handwritten digit recognition, achieving high accuracy with fewer parameters and lower energy consumption. The SNN demonstrates efficient learning and hardware optimization potential.

Keywords:

Approximate computing Neural networks Neuromorphic computing Pattern recognition Spiking neurons Supervised learning

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Area of Science:

Neuromorphic Engineering
Machine Learning
Computational Neuroscience

Background:

Spiking Neural Networks (SNNs) offer a biologically plausible model for computation.
Supervised learning in SNNs is challenging due to non-differentiable spike events.
Efficient hardware implementation of SNNs requires optimization for memory and energy constraints.

Purpose of the Study:

To demonstrate supervised learning in SNNs for handwritten digit recognition.
To optimize SNNs for efficient implementation on constrained hardware.
To compare SNN performance against traditional Artificial Neural Networks (ANNs).

Main Methods:

Utilized the spike triggered Normalized Approximate Descent (NormAD) algorithm for supervised learning.
Employed neurons operating at sparse biological spike rates (<300Hz).
Investigated optimization strategies including neuronal dynamics approximations and reduced precision (3-bit) synaptic weights.

Main Results:

Achieved 98.17% classification accuracy on the MNIST test database.
The SNN used four times fewer parameters than state-of-the-art.
Accuracy degradation was less than 1% with 3-bit synaptic weights.
The SNN outperformed an equivalent ANN trained with backpropagation, especially at low bit precision.

Conclusions:

SNNs trained with precise spike timing show potential for efficient neuromorphic systems.
The NormAD algorithm enables effective supervised learning in SNNs.
Optimized SNNs are suitable for memory and energy-constrained hardware applications.