Melika Payvand

TL;DR: This paper presents a fully neuromorphic sensor fusion approach for hand-gesture recognition comprised of an event-based vision sensor and three different neuromorphic processors, and designed specific spiking neural networks for sensor fusion that showed classification accuracy comparable to the software baseline.

TL;DR: This work demonstrates a hybrid 3D CMOL circuit with 2 layers of memristive crossbars monolithically integrated on a pre-fabricated CMOS substrate, which is the first demonstration of a functional 3DCMOL hybrid circuit.

TL;DR: A neuromorphic implementation of a spiking neural network (SNN) to extract spatio-temporal information of EMG signals locally and classify hand gestures with very low power consumption is proposed.

TL;DR: In this article, the authors provide a tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate Arrays (FPGAs), and Complementary Metal Oxide Semiconductor (CMOS) can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare.

TL;DR: A spiking neural network architecture is presented that supports the use of memristive devices as synaptic elements and a mixed-signal analog-digital interfacing circuits that mitigate the effect of variability in their conductance values and exploit their variability in the switching threshold for implementing stochastic learning are proposed.