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Research Bits: Aug. 8


Speeding NVM encryption Researchers from North Carolina State University propose a way to speed up encryption and file system performance for non-volatile memory (NVM). “NVMs are an emerging technology that allows rapid access to the data, and retains data even when a system crashes or loses power,” said Amro Awad, an assistant professor of electrical and computer engineering at North C... » read more

Neuromorphic Computing: Challenges, Opportunities Including Materials, Algorithms, Devices & Ethics


This new research paper titled "2022 roadmap on neuromorphic computing and engineering" is from numerous researchers at Technical University of Denmark, Instituto de Microelectrónica de Sevilla, CSIC, University of Seville, and many others. Partial Abstract: "The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the chall... » 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

MIT: Stackable AI Chip With Lego-style Design


New technical paper titled "Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence" from researchers at MIT, along with Harvard University, Tsinghua University, Zhejiang University, and others. Partial Abstract: "Here we report stackable hetero-integrated chips that use optoelectronic device arrays for chip-to-chip communication and neuromorphic... » read more

Scalable Approach to Fabricate Memristor Arrays at Wafer-scale


New technical paper titled "Wafer-scale solution-processed 2D material analog resistive memory array for memory-based computing" from researchers at National University of Singapore and Institute of High Performance Computing, Singapore. Abstract "Realization of high-density and reliable resistive random access memories based on two-dimensional semiconductors is crucial toward their develop... » read more

Using Dynamic Route Map Technique for Insight Into Memristors


New technical paper titled "Empirical Characterization of ReRAM Devices Using Memory Maps and a Dynamic Route Map," from Balearic Islands University, UC Berkeley, Health Institute of the Balearic Islands, International Hellenic University, Technische Universität Dresden, Universidad de Valladolid, and Aristotle University of Thessaloniki. Abstract: "Memristors were proposed in the early 1... » 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

Research Bits: March 29


Brain-like AI chip Researchers from Purdue University, Santa Clara University, Portland State University, Pennsylvania State University, Argonne National Laboratory, University of Illinois Chicago, Brookhaven National Laboratory, and University of Georgia built a reprogrammable chip that could be used as the basis for brain-like AI hardware. “The brains of living beings can continuously l... » read more

Experimental photonic quantum memristor


Abstract "Memristive devices are a class of physical systems with history-dependent dynamics characterized by signature hysteresis loops in their input–output relations. In the past few decades, memristive devices have attracted enormous interest in electronics. This is because memristive dynamics is very pervasive in nanoscale devices, and has potentially groundbreaking applications ranging... » read more

Mapping Transformation Enabled High-Performance and Low-Energy Memristor-Based DNNs


Abstract: "When deep neural network (DNN) is extensively utilized for edge AI (Artificial Intelligence), for example, the Internet of things (IoT) and autonomous vehicles, it makes CMOS (Complementary Metal Oxide Semiconductor)-based conventional computers suffer from overly large computing loads. Memristor-based devices are emerging as an option to conduct computing in memory for DNNs to make... » read more

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