Recent Advancements in Emerging Neuromorphic Device Technologies

Woo, Jiyong; Kim, Jeong Hun; Im, Jooyoung; Moon, Seoksu

doi:10.1002/aisy.202000111

Cited by 17 publications

(6 citation statements)

References 103 publications

(128 reference statements)

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“…The advances in neurorobotics and neuromorphic computing will influence the development of the next generation of intelligent agents [565]. Current neuromorphic computing systems already exploit learning and adaptive skills in systems compared to conventional von Neumann machines thanks to non-volatile memories and power efficiency performance [566]. However, new types of sensors and actuators will be introduced to enhance the cognitive and learning functionalities of the systems and deal with safety and robustness concerns.…”

Section: Advances In Science and Technology To Meet Challengesmentioning

confidence: 99%

2022 roadmap on neuromorphic computing and engineering

Christensen

Dittmann

Linares-Barranco

et al. 2022

Neuromorph. Comput. Eng.

282

200

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Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this Roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.

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Section: Advances In Science and Technology To Meet Challengesmentioning

confidence: 99%

2022 roadmap on neuromorphic computing and engineering

Christensen

Dittmann

Linares-Barranco

et al. 2022

Neuromorph. Comput. Eng.

282

200

View full text Add to dashboard Cite

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“…In the last two sections, we have already seen examples of how advanced technologies enable electronic mimics of individual parts of the neural system with demonstrated simple computational functionalities. We do not intend to survey the neuromorphic hardware implementation again as this has been done in numerous reviews, just to name a few, at materials level, [ 48,661–722 ] at device level, [ 10,244,263,723–790 ] at more circuit level, or above. [ …”

Section: Implementation Levelmentioning

confidence: 99%

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Guo

Zou

et al. 2021

Advanced Intelligent Systems

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Neuromorphic electronics, an emerging field that aims for building electronic mimics of the biological brain, holds promise for reshaping the frontiers of information technology and enabling a more intelligent and efficient computing paradigm. As their biological brain counterpart, the neuromorphic electronic systems are complex, having multiple levels of organization. Inspired by David Marr's famous three‐level analytical framework developed for neuroscience, the advances in neuromorphic electronic systems are selectively surveyed and given significance to these research endeavors as appropriate from the computational level, algorithmic level, or implementation level. Under this framework, the problem of how to build a neuromorphic electronic system is defined in a tractable way. In conclusion, the development of neuromorphic electronic systems confronts a similar challenge to the one neuroscience confronts, that is, the limited constructability of the low‐level knowledge (implementations and algorithms) to achieve high‐level brain‐like (human‐level) computational functions. An opportunity arises from the communication among different levels and their codesign. Neuroscience lab‐on‐neuromorphic chip platforms offer additional opportunity for mutual benefit between the two disciplines.

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“…In terms of device functions, neurons, and synaptic behaviors have been successfully simulated. Various synaptic devices have been widely verified as hardware accelerators for artificial neural networks (ANNs), and biological sensing functions have been further developed [19][20][21]. In terms of material systems, devices constructed from various materials, ranging from inorganic to organic, conventional to quantum, and bulk to low-dimensional materials, exhibit distinct neuromorphic characteristics based on their dimensional properties and material compositions [22].…”

Section: Introductionmentioning

confidence: 99%

CMOS-compatible neuromorphic devices for neuromorphic perception and computing: a review

Zhu,

Mao,

Zhu

et al. 2023

Int. J. Extrem. Manuf.

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Neuromorphic computing is a brain-inspired computing paradigm that aims to construct efficient, low-power, and adaptive computing systems by emulating the information processing mechanisms of biological neural systems. At the core of neuromorphic computing are neuromorphic devices that mimic the functions and dynamics of neurons and synapses, enabling the hardware implementation of artificial neural networks. Various types of neuromorphic devices have been proposed based on different physical mechanisms such as resistive switching device and electric-double-layer transistors. These devices have demonstrated a range of neuromorphic functions such as multistate storage, spike-timing-dependent plasticity, and dynamic filtering, etc. To achieve high performance neuromorphic computing systems, it is essential to fabricate neuromorphic devices compatible with complementary metal oxide semiconductor (CMOS) manufacturing process. This improves the device reliability and stability and is favourable to achieve neuromorphic chips with higher integration density and low power consumption. This review summarizes CMOS-compatible neuromorphic devices and discusses their emulation of synaptic and neuronal functions as well as their applications in neuromorphic perception and computing. We highlight challenges and opportunities for further development of CMOS-compatible neuromorphic devices and systems.

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Recent Advancements in Emerging Neuromorphic Device Technologies

Cited by 17 publications

References 103 publications

2022 roadmap on neuromorphic computing and engineering

2022 roadmap on neuromorphic computing and engineering

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

CMOS-compatible neuromorphic devices for neuromorphic perception and computing: a review

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