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Novel Spintronic Neuro-mimetic Device Emulating the LIF Neuron Dynamics w/High Energy Efficiency & Compact Footprints


New technical paper titled "Noise resilient leaky integrate-and-fire neurons based on multi-domain spintronic devices" from researchers at Purdue University. Abstract "The capability of emulating neural functionalities efficiently in hardware is crucial for building neuromorphic computing systems. While various types of neuro-mimetic devices have been investigated, it remains challenging to... » read more

Neuromorphic Chips & Power Demands


Research paper titled "A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware," from researchers at Graz University of Technology and Intel Labs. Abstract "Spike-based neuromorphic hardware holds the promise to provide more energy efficient implementations of Deep Neural Networks (DNNs) than standard hardware such as GPUs. But this requires to understand how D... » read more

Always-On Sub-Microwatt Spiking Neural Network Based on Spike-Driven Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device


Abstract: "This paper presents a novel spiking neural network (SNN) classifier architecture for enabling always-on artificial intelligent (AI) functions, such as keyword spotting (KWS) and visual wake-up, in ultra-low-power internet-of-things (IoT) devices. Such always-on hardware tends to dominate the power efficiency of an IoT device and therefore it is paramount to minimize its power diss... » read more

2D materials–based homogeneous transistor-memory architecture for neuromorphic hardware


Abstract "In neuromorphic hardware, peripheral circuits and memories based on heterogeneous devices are generally physically separated. Thus exploring homogeneous devices for these components is an important issue for improving module integration and resistance matching. Inspired by ferroelectric proximity effect on two-dimensional materials, we present a tungsten diselenide-on-LiNbO3 cascaded... » read more