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The thesis investigates the utilization of memristive and memcapacitive crossbar arrays in low-power machine learning accelerators, offering a comprehensive co-design framework for deep neural networks (DNN). The model, implemented through…

Neural and Evolutionary Computing · Computer Science 2024-03-06 Ankur Singh

Pushing the frontiers of time-series information processing in the ever-growing domain of edge devices with stringent resources has been impeded by the systems' ability to process information and learn locally on the device. Local…

Machine Learning · Computer Science 2024-09-17 Abdullah M. Zyarah , Dhireesha Kudithipudi

Memristive neuromorphic systems are designed to emulate human perception and cognition, where the memristor states represent essential historical information to perform both low-level and high-level tasks. However, current systems face…

Applied Physics · Physics 2024-09-17 Shengbo Wang , Cong Li , Tongming Pu , Jian Zhang , Weihao Ma , Luigi Occhipinti , Arokia Nathan , Shuo Gao

Quantum computer technology harnesses the features of quantum physics for revolutionizing information processing and computing. As such, quantum computers use physical quantum gates that process information unitarily, even though the final…

The emerging mobile devices in this era of internet-of-things (IoT) require a dedicated processor to enable computationally intensive applications such as neuromorphic computing and signal processing. Vector-by-matrix multiplication (VMM)…

Signal Processing · Electrical Eng. & Systems 2020-01-08 Shubham Sahay , Mohammad Bavandpour , Mohammad Reza Mahmoodi , Dmitri Strukov

Memristive crossbar arrays enable in-memory computing by performing parallel analog computations directly within memory, making them well-suited for machine learning, neural networks, and neuromorphic systems. However, despite their…

Cryptography and Security · Computer Science 2025-10-03 Muhammad Faheemur Rahman , Wayne Burleson

Compute-in-memory (CIM) techniques are widely employed in energy-efficient artificial intelligent (AI) processors. They alleviate power and latency bottlenecks caused by extensive data movements between compute and storage units. To extend…

Hardware Architecture · Computer Science 2025-12-15 Jianyi Yu , Tengxiao Wang , Yuxuan Wang , Xiang Fu , Fei Qiao , Ying Wang , Rui Yuan , Liyuan Liu , Cong Shi

Crossbar arrays using emerging non-volatile memory technologies such as Resistive RAM (ReRAM) offer high density, fast access speed and low-power. However the bandwidth of the crossbar is limited to single-bit read/write per access to avoid…

Emerging Technologies · Computer Science 2016-06-03 Mohammad Nasim Imtiaz Khan , Swaroop Ghosh , Radha Krishna Aluru , Rashmi Jha

While two-terminal HfOX (x<2) memristor devices have been studied for ion transport and current evolution, there have been limited reports on the effect of the long-range thermal environment on their performance. In this work,…

The many cores design research community have shown high interest in optical crossbars on chip for more than a decade. Key properties of optical crossbars, namely a) contention-free data routing b) low-latency communication and c) potential…

Emerging Technologies · Computer Science 2015-12-24 Hui Li , Sébastien Le Beux , Gabriela Nicolescu , Ian O 'Connor

Here we demonstrate that both, tunnel magneto resistance (TMR) and resistive switching (RS), can be observed simultaneously in nano-scale magnetic tunnel junctions. The devices show bipolar RS of 6 % and TMR ratios of about 100 %. For each…

Materials Science · Physics 2015-05-13 P. Krzysteczko , G. Reiss , A. Thomas

Current-driven switching of nonvolatile spintronic materials and devices based on spin-orbit torques offer fast data processing speed, low power consumption, and unlimited endurance for future information processing applications. Analogous…

Mesoscale and Nanoscale Physics · Physics 2020-07-23 Nan Zhang , Yi Cao , Yucai Li , Andrew W. Rushforth , Yang Ji , Houzhi Zheng , Kaiyou Wang

Memristors, which are characterized by their unique input-voltage-history-dependent resistance, have garnered significant attention for the exploration of next-generation in-memory computing, reconfigurable logic circuits, and neural…

Emerging non-volatile memory (NVM), or memristive, devices promise energy-efficient realization of deep learning, when efficiently integrated with mixed-signal integrated circuits on a CMOS substrate. Even though several algorithmic…

Neural and Evolutionary Computing · Computer Science 2018-04-23 Vishal Saxena , Xinyu Wu , Kehan Zhu

Since performance improvements of computers are stagnating, new technologies and computer paradigms are hot research topics. Memristor-based In-Memory Computing is one of the promising candidates for the post-CMOS era, which comes in many…

Emerging Technologies · Computer Science 2024-10-22 Fabian Seiler , Nima TaheriNejad

Neuro-symbolic artificial intelligence (AI) excels at learning from noisy and generalized patterns, conducting logical inferences, and providing interpretable reasoning. Comprising a 'neuro' component for feature extraction and a 'symbolic'…

We propose a new family of error detecting stabilizer codes with an encoding rate 1/3 that permit a transversal implementation of the pi/8-rotation $T$ on all logical qubits. The new codes are used to construct protocols for distilling…

Quantum Physics · Physics 2012-11-30 Sergey Bravyi , Jeongwan Haah

Transition-metal-oxide based resistance random access memory is a promising candidate for next-generation universal non-volatile memories. Searching and designing appropriate new materials used in the memories becomes an urgent task. Here,…

Materials Science · Physics 2015-03-05 Linggang Zhu , Jian Zhou , Zhonglu Guo , Zhimei Sun

We analyzed micrometer-scale titanium-niobium-oxide prototype memristors, which exhibited low write-power (<3 {\mu}W) and energy (<200 fJ/bit/{\mu}m2), low read-power (~nW), and high endurance (>millions of cycles). To understand their…

Analog crossbar arrays consisting of emerging memory devices can greatly alleviate the computational strain required by vector matrix multiplications for neural network applications. The ability to produce spin orbit torque-magnetic…

Mesoscale and Nanoscale Physics · Physics 2025-11-05 Samuel Liu , Chen-Yu Hu , Ming-Yuan Song , Xinyu Bao , Jean Anne C. Incorvia
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