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An Adaptive STDP Learning Rule for Neuromorphic Systems.

Ashish Gautam1, Takashi Kohno2

  • 1Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.

Frontiers in Neuroscience
|October 11, 2021
PubMed
Summary

This study introduces an adaptive spike-timing-dependent plasticity (STDP) learning rule using 4-bit synapses for neuromorphic computing. The novel approach achieves high performance in pattern recognition while significantly reducing circuit complexity and power consumption.

Keywords:
adaptive STDPbiomimetic silicon neuronneuromorphic computingneuromorphic hardwarepattern detectionsilicon synapsesynaptic weight resolution

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Area of Science:

  • Neuromorphic Engineering
  • Artificial Intelligence
  • VLSI Design

Background:

  • Neuromorphic computing aims for ultra-low-power intelligent devices by mimicking brain synapses.
  • Spiking neural networks (SNNs) with spike-timing-dependent plasticity (STDP) show promise in pattern detection.
  • Conventional STDP circuits are complex and require significant silicon area.

Purpose of the Study:

  • To introduce a hardware-friendly, modified STDP learning rule using low-bit synapses.
  • To evaluate the performance of this adaptive STDP in a pattern recognition task.
  • To assess the power consumption and silicon area of the proposed circuit.

Main Methods:

  • Implemented an adaptive STDP learning rule utilizing 4-bit synapses.
  • Tested the rule on a neuron recognizing spike patterns in noisy data.
  • Modeled circuits for CMOS neuromorphic applications with mixed-signal learning.

Main Results:

  • The adaptive STDP achieved 94% accuracy, comparable to conventional 64-bit STDP (96%).
  • Static power consumption of a synapse is < 2 pW; energy per spike is < 200 fJ.
  • A single 4-bit synapse with learning circuitry occupies ~17,250 μm².

Conclusions:

  • The proposed 4-bit adaptive STDP offers a hardware-efficient solution for neuromorphic pattern recognition.
  • This approach significantly reduces power consumption and silicon footprint.
  • The adaptive STDP is suitable for developing ultra-low-power intelligent neuromorphic circuits.