Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN.

In this study, we explore spintronic synapses composed of several Magnetic Tunnel Junctions (MTJs), leveraging their attractive characteristics such as endurance, nonvolatility, stochasticity, and energy efficiency for hardware implementation of unsupervised neuromorphic systems. Spiking Neural Networks (SNNs) running on dedicated hardware are suitable for edge computing and IoT devices where continuous online learning and energy efficiency are important characteristics. We focus in this work on synaptic plasticity by conducting comprehensive electrical simulations to optimize the MTJ-based synapse design and find the accurate neuronal pulses that are responsible for the Spike Timing Dependent Plasticity (STDP) behavior.

SRAM-Based PUF Readouts.

Large-scale parameter characterization of Physical Unclonable Functions (PUFs) is of paramount importance in order to assess the quality and thus the suitability of such PUFs which would then be developed as an industrial-grade solution for hardware root of trust. Carrying out a proper characterization requires a large number of devices that need to be repeatedly sampled at various conditions. These prerequisites make PUF characterization process a very time-consuming and expensive task.