In-memory hyperdimensional computing

Karunaratne, Geethan; Gallo, Manuel Le; Cherubini, Giovanni; Benini, Luca; Rahimi, Abbas; Sebastian, Abu

doi:10.1038/s41928-020-0410-3

Cited by 222 publications

(181 citation statements)

References 43 publications

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“…[28] Brain-inspired hyper-dimensional computing that involves the manipulation of large binary vectors has recently emerged as another promising application area for in-memory logic. [29,30] Going beyond binary storage, certain memristive devices can also be programmed to a continuum of resistance or conductance values (analog storage capability). For example, Figure 3b shows a continuum of resistance levels in a PCM device achieved by the application of programming pulses with varying amplitude.…”

Section: In-memory Computingmentioning

confidence: 99%

Memristors—From In‐Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio‐Inspired Computing

Mehonić¹,

Sebastian

Rajendran

et al. 2020

Advanced Intelligent Systems

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197

121

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Three factors are currently driving the main developments in artificial intelligence (AI): availability of vast amounts of data, continuous growth in computing power, and algorithmic innovations. Graphics processing units (GPUs) have been demonstrated as effective co-processors for the implementation of machine learning (ML) algorithms based on deep learning (DL). Solutions based on DL and GPU implementations have led to massive improvements in many AI tasks, but have also caused an exponential increase in demand for computing power. Recent analyses show that the demand for computing power has increased by a factor of 300 000 since 2012, and the estimate is that this demand will double every 3.4 months-at a much faster rate than improvements made historically through Moore's scaling (a sevenfold improvement over the same period of time). [1] At the same time, Moore's law has been slowing down significantly for the last few years, [2] as there are strong indications that we will not be able to continue scaling down complementary metal-oxide-semiconductor (CMOS) transistors. This calls for the exploration of alternative technology roadmaps for the development of scalable and efficient AI solutions. Transistor scaling is not the only way to improve computing performance. Architectural innovations, such as GPUs, field-programmable arrays (FPGAs), and application-specific integrated circuits (ASICs), have all significantly advanced the ML field. [3] A common aspect of modern computing architectures for ML is a move away from the classical von-Neumann architecture that physically separates memory and computing. This approach yields a performance bottleneck that is often the main reason for both energy and speed inefficiency of ML implementations on conventional hardware platforms due to

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Section: In-memory Computingmentioning

confidence: 99%

Memristors—From In‐Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio‐Inspired Computing

Mehonić¹,

Sebastian

Rajendran

et al. 2020

Advanced Intelligent Systems

Self Cite

197

121

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“…This triggers the 'AND' gate and other logic gates such as NAND', 'OR', and 'NOR'. Most recently a complete in-memory hyperdimensional computing (HDC) system was proposed to accomplish a near learning optimum trade-off between design complexity and classification accuracy [130]. Such in-memory HDC mainly consists of an item memory (IM) that stores h, d-dimensional basis hypervectors and an associative memory (AM) that stores c, d-dimensional prototype hypervectors.…”

Section: Potential For Logic Operationsmentioning

confidence: 99%

In-Memory Logic Operations and Neuromorphic Computing in Non-Volatile Random Access Memory

Xiong

et al. 2020

Materials

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Recent progress in the development of artificial intelligence technologies, aided by deep learning algorithms, has led to an unprecedented revolution in neuromorphic circuits, bringing us ever closer to brain-like computers. However, the vast majority of advanced algorithms still have to run on conventional computers. Thus, their capacities are limited by what is known as the von-Neumann bottleneck, where the central processing unit for data computation and the main memory for data storage are separated. Emerging forms of non-volatile random access memory, such as ferroelectric random access memory, phase-change random access memory, magnetic random access memory, and resistive random access memory, are widely considered to offer the best prospect of circumventing the von-Neumann bottleneck. This is due to their ability to merge storage and computational operations, such as Boolean logic. This paper reviews the most common kinds of non-volatile random access memory and their physical principles, together with their relative pros and cons when compared with conventional CMOS-based circuits (Complementary Metal Oxide Semiconductor). Their potential application to Boolean logic computation is then considered in terms of their working mechanism, circuit design and performance metrics. The paper concludes by envisaging the prospects offered by non-volatile devices for future brain-inspired and neuromorphic computation.

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“…Thus, researchers are seeking alternatives to the conventional von Neumann architecture, in which computations can be performed using computing units that are not physically separate from memory units; such technology is termed in-memory computing. [22][23][24][25][26][27][28][29][30][31] However, technologies that are more tangible and plausible and capable of surpassing the computing performance of the conventional von Neumann architecture are also available; these technologies can serve as a platform for increasing the information density per given unit device using ternary, quaternary, or even higher multivalued logic (MVL) systems. [15][16][32][33][34] Current processing systems are based on the binary system, wherein information is stored as a "0" bit or a "1" bit; in contrast, the MVL system functions as a ternary, quaternary, or even higher-valued system, which enables significant reductions in the number of devices and the overall system complexity.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances on Multivalued Logic Gates: A Materials Perspective

Kang

Cho

2021

Advanced Science

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The recent advancements in multivalued logic gates represent a rapid paradigm shift in semiconductor technology toward a new era of hyper Moore's law. Particularly, the significant evolution of materials is guiding multivalued logic systems toward a breakthrough gradually, whereby they are transcending the limits of conventional binary logic systems in terms of all the essential figures of merit, i.e., power dissipation, operating speed, circuit complexity, and, of course, the level of the integration. In this review, recent advances in the field of multivalued logic gates based on emerging materials to provide a comprehensive guideline for possible future research directions are reviewed. First, an overview of the design criteria and figures of merit for multivalued logic gates is presented, and then advancements in various emerging nanostructured materials-ranging from 0D quantum dots to multidimensional heterostructures-are summarized and these materials in terms of device design criteria are assessed. The current technological challenges and prospects of multivalued logic devices are also addressed and major research trends are elucidated.

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In-memory hyperdimensional computing

Cited by 222 publications

References 43 publications

Memristors—From In‐Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio‐Inspired Computing

Memristors—From In‐Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio‐Inspired Computing

In-Memory Logic Operations and Neuromorphic Computing in Non-Volatile Random Access Memory

Recent Advances on Multivalued Logic Gates: A Materials Perspective

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