Benefits Of The Ultra-Low Leakage Currents from IGZO TFTs For Neuromorphic Applications


A new technical paper titled "A tunable multi-timescale Indium-Gallium-Zinc-Oxide thin-film transistor neuron towards hybrid solutions for spiking neuromorphic applications" was published by researchers at imec, CSIC Universidad de Sevilla, and Sungkyunkwan University. Abstract "Spiking neural network algorithms require fine-tuned neuromorphic hardware to increase their effectiveness. Such ... » read more

New Approaches Needed For Power Management


Power is becoming a bigger concern as the amount of data being processed continues to grow, forcing chipmakers and systems companies to rethink compute architectures from the end point all the way to the data center. There is no simple fix to this problem. More data is being collected, moved, and processed, requiring more power at every step, and more attention to physical effects such as he... » read more

Novel Neuromorphic Artificial Neural Network Circuit Architecture


A technical paper titled “Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems” was published by researchers at CEA-LETI Université Grenoble Alpes, University of Zurich and ETH Zurich. Abstract: "The brain’s connectivity is locally dense and globally sparse, forming a small-world graph—a principle prevalent in the evolution of various species, sugg... » read more

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

Analog Circuits Enabling Learning in Mixed-Signal Neuromorphic SNNs, With Tristate Stability and Weight Discretization Circuits


A technical paper titled “Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks” was published by researchers at University of Zurich and ETH Zurich. Abstract: "Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural network circui... » read more

DW-MTJ Devices For Noise-Resilient Networks For Neuromorphic Computing On The Edge


A technical paper titled "Stochastic domain wall-magnetic tunnel junction artificial neurons for noise-resilient spiking neural networks" was published by researchers at University of Texas at Austin. Abstract: "The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) makes SNNs promising for edge applications that require high energy efficiency. To realize SNNs in har... » read more

Recent Developments in Neuromorphic Computing, Focusing on Hardware Design and Reliability


A new technical paper titled "Special Session: Neuromorphic hardware design and reliability from traditional CMOS to emerging technologies" was published by researchers at Univ. Lyon, Ecole Centrale de Lyon, Univ. Grenoble Alpes, Hewlett Packard Labs, CEA-LETI, and Politecnico di Torino. Abstract "The field of neuromorphic computing has been rapidly evolving in recent years, with an incre... » 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

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