Search query: neuromorphic computing

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abstract: The ever increasing processing power demands of digital computers cannot continue to be fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing, which takes inspiration from the highly parallel, low power, high speed, and noise tolerant computing capabilities of the brain, may provide such a shift. Many researchers from across academia and industry have been studying materials, devices, circuits, and systems, to implement some of the functions of networks of neurons and synapses to develop neuromorphic computing platforms. These platforms are being designed using various hardware technologies, including the well established complementary metal oxide semiconductor (CMOS) and emerging memristive technologies such as SiOx based memristors. Herein, recent progress in CMOS, SiOx based memristive, and mixed CMOS memristive hardware for neuromorphic systems is highlighted. New and published results from various devices are provided that are developed to replicate selected functions of neurons, synapses, and simple spiking networks. It is shown that the CMOS and memristive devices are assembled in different neuromorphic learning platforms to perform simple cognitive tasks such as classification of spike rate based patterns or handwritten digits. Herein, it is envisioned that what is demonstrated is useful to the unconventional computing research community by providing insights into advances in neuromorphic hardware technologies.
author_names: Mostafa Rahimi Azghadi

Ying-Chen Chen

Jason Kamran Eshraghian

Jia Chen

Chih-Yang Lin

Amirali Amirsoleimani

Adnan Mehonic

Anthony J Kenyon

Burt W Fowler

Jack C Lee

Yao-Feng Chang
title: Complementary Metal Oxide Semiconductor and Memristive Hardware for Neuromorphic Computing
venue: Advanced Intelligent Systems
year: 2020

abstract: The continued growth in the demand of data storage and processing has spurred the development of high performance storage technologies and brain inspired neuromorphic hardware. Semiconductor quantum dots (QDs) offer an appealing option for these applications since they combine excellent electronic/optical properties and structural stability and can address the requirements of low cost, large area, and solution based manufactured technologies. Here, we focus on the development of nonvolatile memories and neuromorphic computing systems based on QD thin film solids. We introduce recent advances of QDs and highlight their unique electrical and optical features for designing future electronic devices. We also discuss the advantageous traits of QDs for novel and optimized memory techniques in both conventional flash memories and emerging memristors. Then, we review recent advances in QD based neuromorphic devices from artificial synapses to light sensory synaptic platforms. Finally, we highlight major challenges for commercial translation and consider future directions for the postsilicon era.
author_names: Ziyu Lv

Jingrui Chen

Junjie Wang

Ye Zhou

Su-Ting Han
title: Semiconductor Quantum Dots for Memories and Neuromorphic Computing Systems.
venue: Chemical reviews
year: 2020

abstract: As artificial intelligence calls for novel energy efficient hardware, neuromorphic computing systems based on analog resistive switching memory (RSM) devices have drawn great attention recently. Different from the well studied binary RSMs, the analog RSMs are featured by a continuous and controllable conductance tuning ability and thus are capable of combining analog computing and data storage at the device level. Although significant research achievements on analog RSMs have been accomplished, there have been few works demonstrating large scale neuromorphic systems. A major bottleneck lies in the reliability issues of the analog RSM, such as endurance and retention degradation and read/write noises and disturbances. Owing to the complexity of resistive switching mechanisms, studies on the origins of reliability degradation and the corresponding optimization methodology face many challenges. In this article, aiming on the high performance neuromorphic computing applications, we provide a comprehensive review on the status of reliability studies of analog RSMs, the reliability requirements, and evaluation criteria and outlook for future reliability research directions in this field.
author_names: Meiran Zhao

Bin Gao

Jianshi Tang

He Qian

Huaqiang Wu
title: Reliability of analog resistive switching memory for neuromorphic computing
year: 2020

abstract: Functional emulation of biological synapses using electronic devices is regarded as the first step toward neuromorphic engineering and artificial neural networks (ANNs) Electrolyte gated transistors (EGTs) are mixed ionic electronic conductivity devices capable of efficient gate channel capacitance coupling, biocompatibility, and flexible architectures. Electrolyte gating offers significant advantages for the realization of neuromorphic devices/architectures, including ultralow voltage operation and the ability to form parallel interconnected networks with minimal hardwired connectivity. In this review, the most recent developments in EGT based electronics are introduced with their synaptic behaviors and detailed mechanisms, including short /long term plasticity, global regulation phenomena, lateral coupling between device terminals, and spatiotemporal correlated functions. Analog memory phenomena allow for the implementation of perceptron based ANNs. Due to their mixed conductivity phenomena, neuromorphic circuits based on EGTs allow for facile interfacing with biological environments. We also discuss the future challenges in implementing low power, high speed, and reliable neuromorphic computing for large scale ANNs with these neuromorphic devices. The advancement of neuromorphic devices that rely on EGTs highlights the importance of this field for neuromorphic computing and for novel healthcare technologies in the form of adaptable or trainable biointerfacing.
author_names: Haifeng Ling

Dimitrios A Koutsouras

Setareh Kazemzadeh

Yoeri van de Burgt

Feng Yan

Paschalis Gkoupidenis
title: Electrolyte gated transistors for synaptic electronics, neuromorphic computing, and adaptable biointerfacing
year: 2020

abstract: Guided by brain like 'spiking' computational frameworks, neuromorphic computing brain inspired computing for machine intelligence promises to realize artificial intelligence while reducing the energy requirements of computing platforms. This interdisciplinary field began with the implementation of silicon circuits for biological neural routines, but has evolved to encompass the hardware implementation of algorithms with spike based encoding and event driven representations. Here we provide an overview of the developments in neuromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and hardware frameworks. We discuss the main challenges and the future prospects of neuromorphic computing, with emphasis on algorithm hardware codesign. The authors review the advantages and future prospects of neuromorphic computing, a multidisciplinary engineering concept for energy efficient artificial intelligence with brain inspired functionality.
author_names: Kaushik Roy

Akhilesh Jaiswal

Priyadarshini Panda
title: Towards spike based machine intelligence with neuromorphic computing
venue: Nature
year: 2019

abstract: Neuromorphic computing takes inspiration from the brain to create energy efficient hardware for information processing, capable of highly sophisticated tasks. Systems built with standard electronics achieve gains in speed and energy by mimicking the distributed topology of the brain. Scaling up such systems and improving their energy usage, speed and performance by several orders of magnitude requires a revolution in hardware. We discuss how including more physics in the algorithms and nanoscale materials used for data processing could have a major impact in the field of neuromorphic computing. We review striking results that leverage physics to enhance the computing capabilities of artificial neural networks, using resistive switching materials, photonics, spintronics and other technologies. We discuss the paths that could lead these approaches to maturity, towards low power, miniaturized chips that could infer and learn in real time. Neuromorphic computing takes inspiration from the brain to create energy efficient hardware for information processing, capable of highly sophisticated tasks. Including more physics in the algorithms and nanoscale materials used for computing could have a major impact in this field.
author_names: Danijela Markovic

Alice Mizrahi

Damien Querlioz

Julie Grollier
title: Physics for neuromorphic computing
venue: ArXiv
year: 2020

abstract: Abstract Neuromorphic networks that consist of electronic synapses are very important for energy efficient artificial intelligent applications. Therefore, in recent years, many efforts have been made to design and improve artificial synaptic devices to effectively mimic brain spiking activity in biological synapses. In this work, we demonstrate a novel synaptic transistor based on the VO2 film that uses the electrolyte gating at room temperature. Through the gating induced protonation and deprotonation, we realize reversible phase transformations between various H doped phases, which is confirmed by many characterization measurements. The VO2 synaptic transistor based on the exploiting nonvolatile multi level conductance states with various hydrogen doping concentrations can successfully emulate essential synaptic functions such as synaptic plasticity and spiking time dependent plasticity. An artificial neural network containing the VO2 synaptic transistors simulated with supervised learning shows high recognition accuracy for the MNIST handwritten recognition dataset. This study provides a promising approach to develop high performance electronic synaptic transistors by utilizing advanced Mott materials.
author_names: Chen Ge

Ge Li

Qingli Zhou

Jian-yu Du

Er-Jia Guo

Meng He

Can Wang

Guo-zhen Yang

Kuijuan Jin
title: Gating induced reversible HxVO2 phase transformations for neuromorphic computing
year: 2020

abstract: Ionic floating gate memories Digital implementations of artificial neural networks perform many tasks, such as image recognition and language processing, but are too energy intensive for many applications. Analog circuits that use large crossbar arrays of synaptic memory elements represent a low power alternative, but most devices cannot update the synaptic weights uniformly or scale to large array sizes. Fuller et al. developed an integrated device, ionic floating gate memory, that has the gate terminal of a redox transistor electrically connected to a diffusive memristor. This low power device enabled linear and symmetric weight updates in parallel over an entire crossbar array at megahertz rates over 109 write read cycles. Science, this issue p. 570 A low power artificial neural network is enabled by crossbar arrays of redox transistor memories and diffusive memristors. Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and <10 nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating gate memory array based on a polymer redox transistor connected to a conductive bridge memory (CBM) Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents <10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write read operations and support >1 megahertz write read frequencies.
author_names: Elliot J Fuller

Scott Tom Keene

Armantas Melianas

Zhongrui Wang

Sapan Agarwal

Yiyang Li

Yaakov Tuchman

Conrad D James

Matthew J Marinella

J Joshua Yang

Alberto Salleo

A Alec Talin
title: Parallel programming of an ionic floating gate memory array for scalable neuromorphic computing
venue: Science
year: 2019

abstract: Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high density neural networks with ultra low power consumption, novel device concepts combining high scalability, low power operation, and advanced computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS based floating gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.
author_names: Valerio Milo

Gerardo Malavena

Christian Monzio Compagnoni

Daniele Ielmini
title: Memristive and CMOS Devices for Neuromorphic Computing
venue: Materials
year: 2020

abstract: A memristor 1 has been proposed as an artificial synapse for emerging neuromorphic computing applications 2 3 To train a neural network in memristor arrays, changes in weight values in the form of device conductance should be distinct and uniform 3 An electrochemical metallization (ECM) memory 4 5 typically based on silicon (Si) has demonstrated a good analogue switching capability 6 7 owing to the high mobility of metal ions in the Si switching medium 8 However, the large stochasticity of the ion movement results in switching variability. Here we demonstrate a Si memristor with alloyed conduction channels that shows a stable and controllable device operation, which enables the large scale implementation of crossbar arrays. The conduction channel is formed by conventional silver (Ag) as a primary mobile metal alloyed with silicidable copper (Cu) that stabilizes switching. In an optimal alloying ratio, Cu effectively regulates the Ag movement, which contributes to a substantial improvement in the spatial/temporal switching uniformity, a stable data retention over a large conductance range and a substantially enhanced programmed symmetry in analogue conductance states. This alloyed memristor allows the fabrication of large scale crossbar arrays that feature a high device yield and accurate analogue programming capability. Thus, our discovery of an alloyed memristor is a key step paving the way beyond von Neumann computing. Alloying conduction channels of a Si memristor enables stable and controllable device operation with high switching uniformity.
author_names: Hanwool Yeon

Peng Lin

Chanyeol Choi

Scott H Tan

Yongmo Park

Doyoon Lee

Jaeyong Lee

Feng Xu

Bin Gao

Huaqiang Wu

He Qian

Yifan Nie

Seyoung Kim

Jeehwan Kim
title: Alloying conducting channels for reliable neuromorphic computing
venue: Nature Nanotechnology
year: 2020