New Paper: “Optically Tunable Electrical Oscillations in Oxide-Based Memristors for Neuromorphic Computing” led by Collaborator Dr. Shimul K. Nath

optical memristor
The application of hardware-based neural networks can be enhanced by integrating sensory neurons and synapses that enable direct input from external stimuli. Here, we report direct optical control of an oscillatory neuron based on volatile threshold switching in V 3 O 5. The devices exhibit electroforming-free operation with switching parameters that can be tuned by optical illumination. Using temperature-dependent electrical measurements, conductive atomic force microscopy (C-AFM), in-situ thermal imaging, and lumped element modelling, we show that the changes in switching parameters, including threshold and hold voltages, arise from overall conductivity increase of the oxide film due to the contribution of both photo-conductive and bolometric characteristics of V 3 O 5, which eventually affects the oscillation dynamics. Furthermore, our investigation reveals V 3 O 5 as a new bolometric material with a remarkable temperature coefficient of resistivity (TCR) as high as-4.6% K-1 at 423 K. We show the utility of optically tuneable device response and spiking frequency by demonstrating in-sensor reservoir computing with reduced computational effort and an optical encoding layer for spiking neural network, respectively, using a simulated array of devices. This article is protected by copyright. All rights reserved.

New snnTorch Tutorial: Spiking-Tactile MNIST by Undergraduate Students Dylan Louie, Hannah Cohen-Sandler, and Shatoparba Banerjee

See the tutorial here.

The next tutorial from UCSC’s Brain-Inspired Machine Learning class is by Dylan J. LouieHannah Cohen Sandler and Shatoparba Banerjee.

They show how to train an SNN for tactile sensing using the Spiking-Tactile MNIST Neuromorphic Dataset. This dataset was developed in Benjamin C.K. Tee‘s lab in NUS. It consists of handwritten digits obtained by human participants writing on a neuromorphic tactile sensor array.

For more information about the dataset, see the preprint by Hian Hian See et al. here.

 

Prof. Jason Eshraghian and Dr. Fabrizio Ottati Present Tutorial at ISFPGA (Monterey, CA)

Fabrizio Ottati and I will be running a tutorial tomorrow (Sunday, 3 March) at the International Symposium on Field-Programmable Gate Arrays (ISFPGA) in Monterey, CA titled: “Who needs neuromorphic hardware? Deploying SNNs to FPGAs via HLS”.

snn-to-fpga

We’ll go through software and hardware: training SNNs using quantization-aware techniques across weights and stateful quantization, and then show how to go from an snnTorch model straight into AMD/Xilinx FPGAs for low-power + flexible deployment.

GitHub repo: https://github.com/open-neuromorphic/fpga-snntorch

Tutorial summary: https://www.isfpga.org/workshops-tutorials/#t2