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Area- and Energy-Efficient STDP Learning Algorithm for Spiking Neural Network SoC
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DOI：10.1109/ACCESS.2020.3041946.png)
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Abstract
En 中文
Recently, spiking neural networks have gained attention owing to their energy efficiency. All-to-all spike-time dependent plasticity is a popular learning algorithm for spiking neural networks because it is suitable for nondifferentiable spike event-based learning and requires fewer computations than back-propagation-based algorithms. However, the hardware implementation of all-to-all spike-time dependent plasticity is limited by the large storage area required for spike history and large energy consumption caused by frequent memory access. We propose a time-step scaled spike-time dependent plasticity to reduce the storage area required for spike history by reducing the area of the spike-time dependent plasticity learning circuit by 60% and a post-neuron spike-referred spike-time dependent plasticity to reduce the energy consumption by 99.1% by efficiently accessing the memory while learning. The accuracy of Modified National Institute of Standards and Technology image classification degraded by less than 2% when both time-step scaled spike-time dependent plasticity and post-neuron spike-referred spike-time dependent plasticity were applied. Thus, the proposed hardware-friendly spike-time dependent plasticity algorithms make all-to-all spike-time dependent plasticity implementable in more compact areas while reducing energy consumption and experiencing insignificant accuracy degradation.
Keywords:
Neurons
History
Synapses
Artificial neural networks
Firing
Hardware
Biological neural networks
Spike-time dependent plasticity (STDP)
time-step scaled STDP (TS-STDP)
post-neuron spike-referred STDP (PR-STDP)
spiking neural network (SNN)
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