Ionic decision-maker created as novel, solid-state devices

All biological systems, including animals and plants, communicate in a language of ions and small molecules, while the modern information infrastructures and technologies rely on a language of electrons. Although electronics and bioelectronics have made great progress in the past several decades, they still face the disadvantage of signal transformation when communicating with biology. To narrow the gap between biological systems and artificial‐intelligence systems, bioinspired ion‐transport‐based sensory systems should be developed as successor of electronics, since they can emulate biological functionality more directly and communicate with biology seamlessly. Herein, the essential principles of (accurate) ion transport are introduced, and the recent progress in the development of three elements of an ionic sensory system is reviewed: ionic sensors, ionic processors, and ionic interfaces. The current challenges and future developments of ion‐transport‐based sensory systems are also discussed.

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Section: Emergence Of Ion Transport Sensory Paradigmsmentioning

confidence: 99%

Bioinspired Ionic Sensory Systems: The Successor of Electronics

et al. 2020

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“…Such neuromorphic analog computers could adjust deep learning to power-limited context, or even capacitate persistent lifelong learning of a product. Another electrochemistry-based hardware prototype is proposed in [72]. Tsushiya et al design a solid-state ionic device to address decision-making issues like the multiarmed bandit problem (MBPs).…”

Section: Hardware Revolutionmentioning

confidence: 99%

Deep Learning on Computational-Resource-Limited Platforms: A Survey

Chen

Zhang

et al. 2020

Mobile Information Systems

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Nowadays, Internet of Things (IoT) gives rise to a huge amount of data. IoT nodes equipped with smart sensors can immediately extract meaningful knowledge from the data through machine learning technologies. Deep learning (DL) is constantly contributing significant progress in smart sensing due to its dramatic superiorities over traditional machine learning. The promising prospect of wide-range applications puts forwards demands on the ubiquitous deployment of DL under various contexts. As a result, performing DL on mobile or embedded platforms is becoming a common requirement. Nevertheless, a typical DL application can easily exhaust an embedded or mobile device owing to a large amount of multiply and accumulate (MAC) operations and memory access operations. Consequently, it is a challenging task to bridge the gap between deep learning and resource-limited platforms. We summarize typical applications of resource-limited deep learning and point out that deep learning is an indispensable impetus of pervasive computing. Subsequently, we explore the underlying reasons for the high computational overhead of DL through reviewing the fundamental concepts including capacity, generalization, and backpropagation of a neural network. Guided by these concepts, we investigate on principles of representative research works, as well as three types of solutions: algorithmic design, computational optimization, and hardware revolution. In pursuant to these solutions, we identify challenges to be addressed.

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“…These are promising components for the development and construction of new hardware neural networks . Finally, an electrochemical decision‐maker based on hydrogen ion‐conducting polymer (Nafion) has recently been reported with the ability to solve multiarmed bandit problems (MBPs), which clearly illustrates the trend toward integration into logical circuits and artificial intelligence …”

Section: Fundamentals Of Ionotronic Modulationmentioning

confidence: 99%

Section: Building Blocks Of the Bioinspired Ionotronic Systemmentioning

confidence: 99%

The Rise of Bioinspired Ionotronics

Wan

Xiao

Angelin

et al. 2019

Advanced Intelligent Systems

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Ionotronics is a concept which provides powerful tools and methods for narrowing the gap between conventional electronics and biological systems. Translational implementation of ionotronics gives birth to a wide range of novel and exciting paradigm breakers, which greatly innovates the development of the bio–technology interface. Herein, new trends and features of bioinspired ionotronics are presented, first introducing the basic mechanisms of ionotronic modulation and the essential building blocks. Despite its infancy, bioinspired ionotronics exhibit an unprecedented potential to advance a broad spectrum of applications such as robotics, neuromorphic engineering, and biosensing and readouts. Challenges and perspectives for the full exploitation of this concept for practical applications are also addressed. Leveraging bioinspired ionotronics shapes the interaction between human and machines in the future.

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Ionic decision-maker created as novel, solid-state devices

Abstract: Adaptive human decision-making, including collision and mutual concession, is computed by solid-state ionic devices.

Cited by 30 publications

References 35 publications

Bioinspired Ionic Sensory Systems: The Successor of Electronics

Bioinspired Ionic Sensory Systems: The Successor of Electronics

Deep Learning on Computational-Resource-Limited Platforms: A Survey

The Rise of Bioinspired Ionotronics

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