Von Neumann architecture ≈ Von Neumann architecture
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Memory and Information Processing in Neuromorphic Systems Open
A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address…
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Knowledge Enhanced Contextual Word Representations Open
Matthew E. Peters, Mark Neumann, Robert Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, Noah A. Smith. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Nat…
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Memristive technologies for data storage, computation, encryption, and radio-frequency communication Open
Memristive devices, which combine a resistor with memory functions such that voltage pulses can change their resistance (and hence their memory state) in a nonvolatile manner, are beginning to be implemented in integrated circuits for memo…
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Modular Theory in Operator Algebras Open
The first edition of this book appeared in 1981 as a direct continuation of Lectures of von Neumann Algebras (by Ş.V. Strătilă and L. Zsidó) and, until 2003, was the only comprehensive monograph on the subject. Addressing the students of m…
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Accurate deep neural network inference using computational phase-change memory Open
In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a spe…
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An overview of phase-change memory device physics Open
Phase-change memory (PCM) is an emerging non-volatile memory technology that has recently been commercialized as storage-class memory in a computer system. PCM is also being explored for non-von Neumann computing such as in-memory computin…
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A Survey of Neuromorphic Computing and Neural Networks in Hardware Open
Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected syntheti…
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All‐Optically Controlled Memristor for Optoelectronic Neuromorphic Computing Open
Neuromorphic computing (NC) is a new generation of artificial intelligence. Memristors are promising candidates for NC owing to the feasibility of their ultrahigh‐density 3D integration and their ultralow energy consumption. Compared to tr…
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A Survey of Accelerator Architectures for Deep Neural Networks Open
Recently, due to the availability of big data and the rapid growth of computing power, artificial intelligence (AI) has regained tremendous attention and investment. Machine learning (ML) approaches have been successfully applied to solve …
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Optoelectronic Synaptic Devices for Neuromorphic Computing Open
Neuromorphic computing can potentially solve the von Neumann bottleneck of current mainstream computing because it excels at self‐adaptive learning and highly parallel computing and consumes much less energy. Synaptic devices that mimic bi…
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DRISA Open
Data movement between the processing units and the memory in traditional von Neumann architecture is creating the "memory wall" problem. To bridge the gap, two approaches, the memory-rich processor (more on-chip memory) and the compute-cap…
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X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories Open
Silicon-based Static Random Access Memories (SRAM) and digital Boolean logic\nhave been the workhorse of the state-of-art computing platforms. Despite\ntremendous strides in scaling the ubiquitous metal-oxide-semiconductor\ntransistor, the…
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Tutorial: Brain-inspired computing using phase-change memory devices Open
There is a significant need to build efficient non-von Neumann computing systems for highly data-centric artificial intelligence related applications. Brain-inspired computing is one such approach that shows significant promise. Memory is …
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Memristors—From In‐Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio‐Inspired Computing Open
Machine learning, particularly in the form of deep learning (DL), has driven most of the recent fundamental developments in artificial intelligence (AI). DL is based on computational models that are, to a certain extent, bio‐inspired, as t…
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Calculating with light using a chip-scale all-optical abacus Open
Machines that simultaneously process and store multistate data at one and the same location can provide a new class of fast, powerful and efficient general-purpose computers. We demonstrate the central element of an all-optical calculator,…
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A Leaky‐Integrate‐and‐Fire Neuron Analog Realized with a Mott Insulator Open
During the last half century, the tremendous development of computers based on von Neumann architecture has led to the revolution of the information technology. However, von Neumann computers are outperformed by the mammal brain in numerou…
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From biomaterial-based data storage to bio-inspired artificial synapse Open
The implementation of biocompatible and biodegradable information storage would be a significant step toward next-generation green electronics. On the other hand, benefiting from high density, multifunction, low power consumption and multi…
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Challenges in materials and devices for resistive-switching-based neuromorphic computing Open
This tutorial describes challenges and possible avenues for the implementation of the components of a solid-state system, which emulates a biological brain. The tutorial is devoted mostly to a charge-based (i.e. electric controlled) implem…
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Temporal correlation detection using computational phase-change memory Open
Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. As computation becomes increasingly data centric and the scala…
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The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks Open
Spiking neural networks are the basis of versatile and power-efficient information processing in the brain. Although we currently lack a detailed understanding of how these networks compute, recently developed optimization techniques allow…
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A 4-fJ/Spike Artificial Neuron in 65 nm CMOS Technology Open
As Moore's law reaches its end, traditional computing technology based on the Von Neumann architecture is facing fundamental limits. Among them is poor energy efficiency. This situation motivates the investigation of different processing i…
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Maximum Entropy Models for Quantum Systems Open
We show that for a finite von Neumann algebra, the states that maximise Segal’s entropy with a given energy level are Gibbs states. This is a counterpart of the classical result for the algebra of all bounded linear operators on a Hilbert …
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A review of memristor: material and structure design, device performance, applications and prospects Open
With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing…
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In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and Perspectives Open
The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigm…
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Roadmap on emerging hardware and technology for machine learning Open
Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy …
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Hardware Implementation of Neuromorphic Computing Using Large‐Scale Memristor Crossbar Arrays Open
Brain‐inspired neuromorphic computing is a new paradigm that holds great potential to overcome the intrinsic energy and speed issues of traditional von Neumann based computing architecture. With the ability to perform vector‐matrix multipl…
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Memristive Crossbar Arrays for Storage and Computing Applications Open
The emergence of memristors with potential applications in data storage and artificial intelligence has attracted wide attentions. Memristors are assembled in crossbar arrays with data bits encoded by the resistance of individual cells. De…
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Pathways to efficient neuromorphic computing with non-volatile memory technologies Open
Historically, memory technologies have been evaluated based on their storage density, cost, and latencies. Beyond these metrics, the need to enable smarter and intelligent computing platforms at a low area and energy cost has brought forth…
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Redox gated polymer memristive processing memory unit Open
Memristors with enormous storage capacity and superior processing efficiency are of critical importance to overcome the Moore’s Law limitation and von Neumann bottleneck problems in the big data and artificial intelligence era. In particul…
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Universal Memcomputing Machines Open
We introduce the notion of universal memcomputing machines (UMMs): a class of brain-inspired general-purpose computing machines based on systems with memory, whereby processing and storing of information occur on the same physical location…