Spiking Hardware for neuromorhic computing
Bernabé Linares-Barranco a
a CSIC and Univ. de Sevilla, Av. Américo Vespucio 28, Sevilla, Spain
nanoGe Fall Meeting
Proceedings of Materials for Sustainable Development Conference (MAT-SUS) (NFM22)
Neuromorphic Sensory-Processing-Learning Systems inspired in Computation Neuroscience
Barcelona, Spain, 2022 October 24th - 28th
Organizers: Bernabé Linares Barranco and Timothée Masquelier
Invited Speaker, Bernabé Linares-Barranco, presentation 258
DOI: https://doi.org/10.29363/nanoge.nfm.2022.258
Publication date: 11th July 2022

We will briefly give an overview of vision with Dynamic Vision Sensor (DVS) cameras, processing with spiking-based hardware processing modules, and link it with emerging nanoscale synaptic-like devices which can exploit on-line bio-inspired learning. DVS cameras are frame-free strongly bio-inspired vision sensors that result in highly energy efficient visual information encoding, very well suited for processing with spiking neural networks. We will present techniques to process such signals with spiking neural network hardware that can be modularly expanded to scaled-up systems. Spike Timing Dependent Plasticity (STDP) is one type of learning rule for Spiking Neural Networks (SNN). We will present how STDP can be implemented by exploiting novel nano-scale memristor devices, used as synapses, whose resistance changes as correlated spiking signals appear at their terminals. We will show experimental results from a CMOS chip with 4k monolithically integrated nanoscale memristors performing spiking computation and recognition of spiking patterns. 

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