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The input-dependent variable sampling (I-DEVS) energy-efficient digital neuron implementation method
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DOI：10.1007/s11071-023-08394-x.png)
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Abstract
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A method is proposed by which the power consumption of a biologically detailed digital neuron implementation can be reduced without modification to the digital neuron's hardware architecture and independent of the neuron model. This method results in substantial power savings by causing the neuron to enter a quasi-functional state when low input stimulus is received. This approach is analogous to the function of real biological neurons as they enter a low-activity state for low stimulus. The shifts in neuronal activity created by the novel method allow for the membrane potential to remain uncorrupted over a large domain of input synaptic current, while avoiding unnecessary computations and switching activity. The digital hardware implementation results are presented and discussed, and it is shown that the behaviour of the neuron is unaffected using the novel method. The power consumption of the implemented digital neurons is compared with traditional implementations, and considerable power savings are shown.
Keywords:
Neuromorphic systems
Spiking neurons
Bio-inspired computation
Digital hardware
Journal
IF:
6
Papers:
1.4W
Citations:
5.0W
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Design of silicon brains in the nano-CMOS era: Spiking neurons, learning synapses and neural architecture optimization.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
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