tag: artificial neural networks

ANN Framework for Thermal-Aware Modeling of GAAFETs (NYCU)
By Technical Paper Link - 12 May, 2026 - Comments: 0

A new technical paper, "A Device-Physics-Informed Artificial Neural Network Approach for Thermal-Aware I-V and C-V Modeling of GAA FETs," was published by researchers at National Yang Ming Chiao Tung University. Abstract "This work introduces a device-physics-informed neural network framework for simultaneous modeling of thermal-aware I-V and C-V characteristics of gate-all-around (GAA) f... » read more

Neuromorphic Computing: Memristor Based On Vertically Aligned Nanocomposite With Highly Defective Vertical Channels (Purdue, UT Arlington)
By Technical Paper Link - 12 Sep, 2025 - Comments: 0

A new technical paper titled "An Ultra-Robust Memristor Based on Vertically Aligned Nanocomposite with Highly Defective Vertical Channels for Neuromorphic Computing" was published by researchers at Purdue University and University of Texas at Arlington. "In this work, a memristor based on SrTiO3-CeO2 (S-C) VAN thin films with highly defective vertical interfaces has been successfully demonst... » read more

Optical Next-Gen Reservoir Computing Framework (Sorbonne, CNRS, Tsinghua U. et al)
By Technical Paper Link - 05 Aug, 2025 - Comments: 0

A new technical paper titled "Optical next generation reservoir computing" was published by researchers at Sorbonne Université, CNRS, Tsinghua University, University of Hong Kong, and University of Tokyo. Excerpt "Artificial neural networks with internal dynamics exhibit remarkable capability in processing information. Reservoir computing (RC) is a canonical example that features rich comp... » read more

Scalable And Energy Efficient Solution for Hardware-Based ANNs (KAUST, NUS)
By Technical Paper Link - 30 Mar, 2025 - Comments: 0

A new technical paper titled "Synaptic and neural behaviours in a standard silicon transistor" was published by researchers at KAUST and National University of Singapore. Abstract "Hardware implementations of artificial neural networks (ANNs)—the most advanced of which are made of millions of electronic neurons interconnected by hundreds of millions of electronic synapses—have achieved ... » read more

HW Implementation of Memristive ANNs
By Technical Paper Link - 22 Mar, 2024 - Comments: 0

A new technical paper titled "Hardware implementation of memristor-based artificial neural networks" was published by KAUST, Universitat Autònoma de Barcelona, IBM Research, USC, University of Michigan and others. Abstract: "Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units oper... » read more

Recent Developments in Neuromorphic Computing, Focusing on Hardware Design and Reliability
By Technical Paper Link - 08 May, 2023 - Comments: 0

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

2D-Materials-Based Electronic Circuits (KAUST and TSMC)
By Technical Paper Link - 06 Dec, 2022 - Comments: 0

A special edition article titled "Electronic Circuits made of 2D Materials" was just published by Dr. Mario Lanza, KAUST Associate Professor of Material Science and Engineering, and Iuliana Radu, corporate researcher at TSMC. This special issue covers 21 articles from leading subject matter experts, ranging from materials synthesis and their integration in micro/nano-electronic devices and c... » read more

3 Emerging Technologies: Memristors, Spintronics & 2D Materials
By Technical Paper Link - 06 Jul, 2022 - Comments: 0

New technical paper titled "Memristive, Spintronic, and 2D-Materials-Based Devices to Improve and Complement Computing Hardware" from researchers at University College London and University of Cambridge. Abstract "In a data-driven economy, virtually all industries benefit from advances in information technology—powerful computing systems are critically important for rapid technological pr... » read more

AI-Powered Verification
By Brian Bailey - 01 Jun, 2022 - Comments: 1

With functional verification consuming more time and effort than design, the chip industry is looking at every possible way to make the verification process more effective and more efficient. Artificial intelligence (AI) and machine learning (ML) are being tested to see how big an impact they can have. While there is progress, it still appears to be just touching the periphery of the problem... » read more

Easier And Faster Ways To Train AI
By Bryon Moyer - 04 Nov, 2021 - Comments: 0

Training an AI model takes an extraordinary amount of effort and data. Leveraging existing training can save time and money, accelerating the release of new products that use the model. But there are a few ways this can be done, most notably through transfer and incremental learning, and each of them has its applications and tradeoffs. Transfer learning and incremental learning both take pre... » read more

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