Analog Planar Memristor Device: Developing, Designing, and Manufacturing

A new technical paper titled "Analog monolayer SWCNTs-based memristive 2D structure for energy-efficient deep learning in spiking neural networks" was published by researchers at Delft University of Technology and Khalifa University. Abstract: "Advances in materials science and memory devices work in tandem for the evolution of Artificial Intelligence systems. Energy-efficient computation... » read more

A Survey Of Recent Advances In Spiking Neural Networks From Algorithms To HW Acceleration

A technical paper titled “Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technology” was published by researchers at Intel Labs, University of California Santa Cruz, University of Wisconsin-Madison, and University of Southern California. Abstract: "Neuromorphic computing and, in particular, spiking neural networks (SNNs) have becom... » read more

Neuromorphic Computing: Graphene-Based Memristors For Future AI Hardware From Fabrication To SNNs

A technical paper titled “A Review of Graphene-Based Memristive Neuromorphic Devices and Circuits” was published by researchers at James Cook University (Australia) and York University (Canada). Abstract: "As data processing volume increases, the limitations of traditional computers and the need for more efficient computing methods become evident. Neuromorphic computing mimics the brain's... » read more

Spiking Neural Networks: Hardware & Algorithm Developments

A new technical paper titled "Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware" was published by researchers at Purdue University, Pennsylvania State University, and Yale University. Excerpt from Abstract: "In this article, we outline several strides that neuromorphic computing based on spiking neural networks (SNNs) has taken over the recent past, a... » read more

MEMprop: Gradient-based Learning To Train Fully Memristive SNNs

New technical paper titled "Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays" from IEEE researchers. Abstract "We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs). Our approach harnesses intrinsic device dynamics to trigger naturally arising voltage spikes. These spikes emitted by memristive dynamics are anal... » read more

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

Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems

New academic paper from Washington State University, supported by a grant from the National Science Foundation. Abstract: "Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP),... » 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

An Event-Driven and Fully Synthesizable Architecture for Spiking Neural Networks

Abstract:  "The development of brain-inspired neuromorphic computing architectures as a paradigm for Artificial Intelligence (AI) at the edge is a candidate solution that can meet strict energy and cost reduction constraints in the Internet of Things (IoT) application areas. Toward this goal, we present μBrain: the first digital yet fully event-driven without clock architecture, with co-lo... » read more

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