Stochasticity in Neuromorphic Computing: Evaluating Randomness for Improved Performance
| Autor: | Garrett S. Rose, Musabbir Adnan, Sakib Hasan, Gangotree Chakma, Samuel D. Brown |
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| Rok vydání: | 2019 |
| Předmět: |
Spiking neural network
Quantitative Biology::Neurons and Cognition Noise (signal processing) Computer science Generalization Information processing Topology (electrical circuits) 02 engineering and technology 021001 nanoscience & nanotechnology 03 medical and health sciences Improved performance Computer Science::Emerging Technologies 0302 clinical medicine Neuromorphic engineering Computer engineering 0210 nano-technology 030217 neurology & neurosurgery Randomness |
| Zdroj: | ICECS |
| DOI: | 10.1109/icecs46596.2019.8965057 |
| Popis: | Several researchers have proposed that random noise in highly non-linear biological systems helps with learning and information processing. Neuromorphic systems could potentially harness the computational power of such biological systems by utilizing stochasticity to imitate random biological noise. Systems implementing this kind of probabilistic behavior would open up an entirely new realm of potential algorithms and applications to neuromorphic developers. This paper proposes an efficient and reliable stochastic spiking neuromorphic topology that utilizes variations of the membrane capacitance to realize stochastic behavior. We also present analyses of network-level effects of this stochastic spiking neuron behavior, showing the performance impact on classification and other neuromorphic applications. This network-level analysis shows that stochastic noise does in fact provide powerful generalization properties that improve performance in emerging neuromorphic systems. |
| Databáze: | OpenAIRE |
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