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Nonlinear dynamical systems useful for reservoir computing enhance the physical implementation of computing systems. In this paper we show that electrochemical reactions and their versatility show great potential as reservoirs. The essence of signal processing in such systems is the different levels of ionic current passing through the solution and the detected electrochemical current based on a multidirectional data acquisition system. A basic electrochemical system is also tested to verify the distinction between Faradic and non-Faradic current. Faradic current exhibits a nonlinear effect on the applied voltage and increase the high-dimensional memory capacity. [ABSTRACT FROM AUTHOR]

Magnetic skyrmions are topological spin textures, with great potential for energy-saving next generation devices such as non-volatile memory devices. Recently, the application of skyrmions to neuromorphic devices, including physical reservoir computing devices, has attracted much attention. In this article, we introduce the recent progress in research on skyrmion-based physical reservoir computing, mainly focusing on our results. We recently demonstrated physical reservoir computing using the nonlinear response originating from the magnetic-field induced dynamics of skyrmions formed in ultrathin multilayer films. We succeeded in waveform recognition and handwritten digit recognition. Notably, there is a positive correlation between the recognition accuracy and the number of skyrmions in the devices. In addition to our results, skyrmion-based reservoir computing devices have also been reported by other groups, highlighting the high potential of magnetic skyrmions for physical reservoir computing. [ABSTRACT FROM AUTHOR]

Physical reservoir computing (PRC) with visible-light signals was demonstrated using dye-sensitized solar cells. The short-term memory required for PRC was confirmed using light pulse inputs. Waveform learning was demonstrated for nonlinear autoregressive moving-average time series level 2 (NARMA2) signals with normalized mean square error of 0.027. The relatively slow (milliseconds to seconds) and complex charge transfer dynamics in the TiO2 porous layer with redox reactions in the solution phase provided the characteristics required for PRC. [ABSTRACT FROM AUTHOR]

Abstract Physical reservoir computing is a promising solution for accelerating artificial intelligence (AI) computations. Various physical systems that exhibit nonlinear and fading-memory properties have been proposed as physical reservoirs. Highly-integrable physical reservoirs, particularly for edge AI computing, has a strong demand. However, realizing a practical physical reservoir with high performance and integrability remains challenging. Herein, we present an analogue circuit reservoir with a simple cycle architecture suitable for complementary metal-oxide-semiconductor (CMOS) chip integration. In several benchmarks and demonstrations using synthetic and real-world data, our developed hardware prototype and its simulator exhibit a high prediction performance and sufficient memory capacity for practical applications, showing promise for future applications in highly integrated AI accelerators.

Physical reservoir computing is a promising solution for accelerating artificial intelligence (AI) computations. Various physical systems that exhibit nonlinear and fading-memory properties have been proposed as physical reservoirs. Highly-integrable physical reservoirs, particularly for edge AI computing, has a strong demand. However, realizing a practical physical reservoir with high performance and integrability remains challenging. Herein, we present an analogue circuit reservoir with a simple cycle architecture suitable for complementary metal-oxide-semiconductor (CMOS) chip integration. In several benchmarks and demonstrations using synthetic and real-world data, our developed hardware prototype and its simulator exhibit a high prediction performance and sufficient memory capacity for practical applications, showing promise for future applications in highly integrated AI accelerators. [ABSTRACT FROM AUTHOR]

Health monitoring using wearable artificial intelligence (AI) sensors with sensing and cognitive capabilities has garnered significant attention. The development of self‐contained AI sensors that can operate with low power consumption, akin to the human brain, is necessary. Physical reservoir computing (PRC), which mimics the human brain using physical phenomena, offers a low‐power consumption architecture. Nevertheless, creating a flexible and easily disposable sensors using PRC capable of processing optical signals with sub‐second response times suitable for biological signals presents a challenge. In this study, a disposable and flexible paper‐based optoelectronic synaptic devices are designed, which are composed of nanocellulose and ZnO nanoparticles, for PRC. This device exhibits synaptic photocurrent in response to optical input. To assess its performance, a classification and time‐series forecasting tasks are conducted. The memory capacity of short‐term memory task, indicating the device's ability to store past information, is 1.8. The device can recognize handwritten digits with an accuracy of 88%. These results highlight the potential of the device for PRC. In addition, subjecting the device to 1000 rounds of bending do not affect its accuracy. Furthermore, the device burn in a few seconds, much like regular office paper, demonstrating its disposability. [ABSTRACT FROM AUTHOR]

Recent advances in autonomous systems have prompted a strong demand for the next generation of adaptive structures and materials to possess built‐in intelligence in their mechanical domain, the so‐called mechano‐intelligence (MI). Previous MI attempts mainly focused on specific case studies and lacked a systematic foundation in effectively and efficiently constructing and integrating different intelligent functions. Here, a new approach is uncovered to create multifunctional MI in adaptive structures using physical reservoir computing (PRC). That is, to concurrently embody computing power and the key elements of intelligence, namely perception, decision‐making, and commanding, directly in the mechanical domain, advancing from conventional reliance on add‐on computers and massive electronics. As an exemplar platform, a mechanically intelligent phononic metastructure is developed by harnessing its high‐degree‐of‐freedom nonlinear dynamics as PRC power. Through analyses and experiments, multiple intelligent structural functions are demonstrated ranging from self‐tuning wave controls to wave‐based logic gates. This research provides the much‐needed basis for creating future smart structures and materials that greatly surpass the state of the art—such as lower power consumption, more direct interactions, and better survivability in harsh environments or under cyberattacks. Moreover, it enables the addition of new functions and autonomy to systems without overburdening the onboard computers. [ABSTRACT FROM AUTHOR]

Reservoir computing is a highly efficient machine learning framework for processing temporal data by extracting input features and mapping them into higher dimensional spaces. Physical reservoirs have been realized using spintronic oscillators, atomic switch networks, volatile memristors, etc. However, these devices are intrinsically energy‐dissipative due to their resistive nature, increasing their power consumption. Therefore, memcapacitive devices can provide a more energy‐efficient approach. Herein, volatile biomembrane‐based memcapacitors are leveraged as reservoirs to solve classification tasks and process time series in simulation and experimentally. This system achieves a 99.6% accuracy for spoken‐digit classification and a normalized mean square error of 7.81×10−4$7.81 \times \left(10\right)^{- 4}$ in a second‐order nonlinear regression task. Furthermore, to showcase the device's real‐time temporal data processing capability, a 100% accuracy for an epilepsy detection problem is achieved. Most importantly, it is demonstrated that each memcapacitor consumes an average of 41.5 fJ of energy per spike, regardless of the selected input voltage pulse width, while maintaining an average power of 415 fW for a pulse width of 100 ms, orders of magnitude lower than those achieved by state‐of‐the‐art devices. Lastly, it is believed that the biocompatible, soft nature of our memcapacitor renders it highly suitable for computing applications in biological environments. [ABSTRACT FROM AUTHOR]

Physical reservoir computing, which is a promising method for the implementation of highly efficient artificial intelligence devices, requires a physical system with nonlinearity, fading memory, and the ability to map in high dimensions. Although it is expected that spin wave interference can perform as highly efficient reservoir computing in some micromagnetic simulations, there has been no experimental verification to date. Herein, reservoir computing is demonstrated that utilizes multidetected nonlinear spin wave interference in an yttrium‐iron‐garnet single crystal. The subject computing system achieves excellent performance when used for hand‐written digit recognition, second‐order nonlinear dynamical tasks, and nonlinear autoregressive moving average (NARMA). It is of particular note that normalized mean square errors for NARMA2 and second‐order nonlinear dynamical tasks are 1.81 × 10−2 and 8.37 × 10−5, respectively, which are the lowest figures for any experimental physical reservoir so far reported. Said high performance is achieved with higher nonlinearity and the large memory capacity of interfered spin wave multidetection. [ABSTRACT FROM AUTHOR]

Abstract Health monitoring using wearable artificial intelligence (AI) sensors with sensing and cognitive capabilities has garnered significant attention. The development of self‐contained AI sensors that can operate with low power consumption, akin to the human brain, is necessary. Physical reservoir computing (PRC), which mimics the human brain using physical phenomena, offers a low‐power consumption architecture. Nevertheless, creating a flexible and easily disposable sensors using PRC capable of processing optical signals with sub‐second response times suitable for biological signals presents a challenge. In this study, a disposable and flexible paper‐based optoelectronic synaptic devices are designed, which are composed of nanocellulose and ZnO nanoparticles, for PRC. This device exhibits synaptic photocurrent in response to optical input. To assess its performance, a classification and time‐series forecasting tasks are conducted. The memory capacity of short‐term memory task, indicating the device's ability to store past information, is 1.8. The device can recognize handwritten digits with an accuracy of 88%. These results highlight the potential of the device for PRC. In addition, subjecting the device to 1000 rounds of bending do not affect its accuracy. Furthermore, the device burn in a few seconds, much like regular office paper, demonstrating its disposability.

Reservoir computing is a highly efficient machine learning framework for processing temporal data by extracting input features and mapping them into higher dimensional spaces. Physical reservoirs have been realized using spintronic oscillators, atomic switch networks, volatile memristors, etc. However, these devices are intrinsically energy‐dissipative due to their resistive nature, increasing their power consumption. Therefore, memcapacitive devices can provide a more energy‐efficient approach. Herein, volatile biomembrane‐based memcapacitors are leveraged as reservoirs to solve classification tasks and process time series in simulation and experimentally. This system achieves a 99.6% accuracy for spoken‐digit classification and a normalized mean square error of 7.81×10−4 in a second‐order nonlinear regression task. Furthermore, to showcase the device's real‐time temporal data processing capability, a 100% accuracy for an epilepsy detection problem is achieved. Most importantly, it is demonstrated that each memcapacitor consumes an average of 41.5 fJ of energy per spike, regardless of the selected input voltage pulse width, while maintaining an average power of 415 fW for a pulse width of 100 ms, orders of magnitude lower than those achieved by state‐of‐the‐art devices. Lastly, it is believed that the biocompatible, soft nature of our memcapacitor renders it highly suitable for computing applications in biological environments.

Physical reservoir computing, which is a promising method for the implementation of highly efficient artificial intelligence devices, requires a physical system with nonlinearity, fading memory, and the ability to map in high dimensions. Although it is expected that spin wave interference can perform as highly efficient reservoir computing in some micromagnetic simulations, there has been no experimental verification to date. Herein, reservoir computing is demonstrated that utilizes multidetected nonlinear spin wave interference in an yttrium‐iron‐garnet single crystal. The subject computing system achieves excellent performance when used for hand‐written digit recognition, second‐order nonlinear dynamical tasks, and nonlinear autoregressive moving average (NARMA). It is of particular note that normalized mean square errors for NARMA2 and second‐order nonlinear dynamical tasks are 1.81 × 10−2 and 8.37 × 10−5, respectively, which are the lowest figures for any experimental physical reservoir so far reported. Said high performance is achieved with higher nonlinearity and the large memory capacity of interfered spin wave multidetection.

Abstract Recent advances in autonomous systems have prompted a strong demand for the next generation of adaptive structures and materials to possess built‐in intelligence in their mechanical domain, the so‐called mechano‐intelligence (MI). Previous MI attempts mainly focused on specific case studies and lacked a systematic foundation in effectively and efficiently constructing and integrating different intelligent functions. Here, a new approach is uncovered to create multifunctional MI in adaptive structures using physical reservoir computing (PRC). That is, to concurrently embody computing power and the key elements of intelligence, namely perception, decision‐making, and commanding, directly in the mechanical domain, advancing from conventional reliance on add‐on computers and massive electronics. As an exemplar platform, a mechanically intelligent phononic metastructure is developed by harnessing its high‐degree‐of‐freedom nonlinear dynamics as PRC power. Through analyses and experiments, multiple intelligent structural functions are demonstrated ranging from self‐tuning wave controls to wave‐based logic gates. This research provides the much‐needed basis for creating future smart structures and materials that greatly surpass the state of the art—such as lower power consumption, more direct interactions, and better survivability in harsh environments or under cyberattacks. Moreover, it enables the addition of new functions and autonomy to systems without overburdening the onboard computers.

One of the new computational frameworks is physical reservoir computing. Focusing on this method, we have previously developed a soft-matter artificial mouth "Gel Biter", which is composed of multiple polymeric materials based on the structure of the human oral cavity. This soft machine can discriminate even subtle differences in food texture with high accuracy. In general, chewing speed differs from person to person. Then, we focus on the result that brittle foods tend to be chewed faster or more finely based on sensory evaluation in some cognitive studies. This study has analyzed the accuracy of the Gel Biter by changing the parameters of its robotic arm and the differences in food texture perceived when the chewing speed is changed. As a result, there is no significant difference in discrimination accuracy for each speed. The cluster analysis shows that the food characteristics are captured and classified. In addition, the estimation results for Fast chewing indicate that the mechanical mouth also generates the illusion that humans perceive different food textures. [ABSTRACT FROM AUTHOR]

Producing original and arranging existing musical outcomes is an art that takes years of learning and practice to master. Yet, despite the constant advances in the field of AI-powered musical creativity, production of quality musical outcomes remains a prerogative of the humans. Here we demonstrate that a single bubble in water can be used to produce creative musical outcomes, when it nonlinearly oscillates under an acoustic pressure signal that encodes a piece of classical music. The audio signal of the response of the bubble resembles an electric guitar version of the original composition. We suggest, and provide plausible theoretical supporting arguments, that this property of the bubble can be used to create physics-inspired AI systems capable of simulating human creativity in arrangement and composition of music. [ABSTRACT FROM AUTHOR]

Physical reservoir computing is a framework that enables energy‐efficient information processing by using physical systems. Nonlinear dynamics in physical systems provide a computational capability that is unique to reservoirs. It is, however, difficult to find an appropriate task for a reservoir because of the complexity of nonlinear information processing. The information processing capacity has recently been used to clarify systematically the tasks that are solved by reservoirs; it quantifies the memory capacity of reservoirs in accordance with the order of nonlinearity. Herein, an experimental evaluation of the information processing capacity of a spintronic oscillator consisting of nanostructured ferromagnets is reported. The spintronic reservoir state is electrically manipulated by adding a delayed‐feedback circuit. The total capacity reaches a maximum of 5.6 at the edge of the echo state property. A trade‐off between the linear and nonlinear components of the capacity is also found. The result can be used to better understand the nonlinear information processing in reservoirs and to find good matches between reservoirs and tasks. As an example, a function‐approximation task is performed and it is found that it can be efficiently solved when the reservoir state is appropriately tuned so that its information processing capacity matches that of the task. [ABSTRACT FROM AUTHOR]

Physical reservoir computing is a framework that enables energy‐efficient information processing by using physical systems. Nonlinear dynamics in physical systems provide a computational capability that is unique to reservoirs. It is, however, difficult to find an appropriate task for a reservoir because of the complexity of nonlinear information processing. The information processing capacity has recently been used to clarify systematically the tasks that are solved by reservoirs; it quantifies the memory capacity of reservoirs in accordance with the order of nonlinearity. Herein, an experimental evaluation of the information processing capacity of a spintronic oscillator consisting of nanostructured ferromagnets is reported. The spintronic reservoir state is electrically manipulated by adding a delayed‐feedback circuit. The total capacity reaches a maximum of 5.6 at the edge of the echo state property. A trade‐off between the linear and nonlinear components of the capacity is also found. The result can be used to better understand the nonlinear information processing in reservoirs and to find good matches between reservoirs and tasks. As an example, a function‐approximation task is performed and it is found that it can be efficiently solved when the reservoir state is appropriately tuned so that its information processing capacity matches that of the task.

More than 3.5 billion people live in rural areas, where water and water energy resources play an important role in ensuring sustainable and productive rural economies. This article reviews and critically analyses the recent advances in the field of analogue and reservoir computing that have been driven by the unique physical properties and energy of water waves. It also demonstrates that analogue and physical reservoir computing, taken as an independent research field, holds the potential to bring artificial intelligence closer to people living outside large cities, thus enabling them to enjoy the benefits of novel technologies that are already in place in large cities but are not readily available or suitable for regional communities. In particular, although the physical reservoir computing systems discussed in the main text are universal in terms of processing input data and making forecasts, they can be used to design and optimise power grid networks and forecast energy consumption, both at local and global scales. Thus, this review article will be of interest to a broad readership interested in novel concepts of artificial intelligence and machine learning and their innovative practical applications in diverse areas of science and technology. [ABSTRACT FROM AUTHOR]

Physical reservoir computing is a computational paradigm that enables spatiotemporal pattern recognition to be performed directly in matter. The use of physical matter leads the way toward energy‐efficient devices capable of solving machine learning problems without having to build a system of millions of interconnected neurons. Proposed herein is a high‐performance "skyrmion mixture reservoir" that implements the reservoir computing model with multidimensional inputs. This implementation solves spoken digit classification tasks with an overall model accuracy of 97.4% and a < 1% word error ratethe best performance ever reported for in materio reservoir computers. Due to the quality of the results and the low‐power properties of magnetic texture reservoirs, it is evident that skyrmion fabrics are a compelling candidate for reservoir computing. [ABSTRACT FROM AUTHOR]

Physical reservoir computing is a computational paradigm that enables spatiotemporal pattern recognition to be performed directly in matter. The use of physical matter leads the way toward energy‐efficient devices capable of solving machine learning problems without having to build a system of millions of interconnected neurons. Proposed herein is a high‐performance “skyrmion mixture reservoir” that implements the reservoir computing model with multidimensional inputs. This implementation solves spoken digit classification tasks with an overall model accuracy of 97.4% and a

Physical reservoir computing (PRC) with visible-light signals was demonstrated using dye-sensitized solar cells. The short-term memory required for PRC was confirmed using light pulse inputs. Waveform learning was demonstrated for nonlinear autoregressive moving-average time series level 2 (NARMA2) signals with normalized mean square error of 0.027. The relatively slow (milliseconds to seconds) and complex charge transfer dynamics in the TiO _2 porous layer with redox reactions in the solution phase provided the characteristics required for PRC.

Physical Reservoir Computing (PRC) is a technology attracting attention for overcoming the limitations of conventional digital computing. This technology is particularly useful in areas where complex nonlinear problems and real-time processing are required. PRC is a technique using physical systems (e.g., optical, electronic, magnetic, and mechanical systems) as reservoirs and utilizes their dynamic properties for computation. This enables efficient processing of tasks such as speech recognition, image processing, and predictive modeling. We asked top researchers to contribute their recent research to this special issue about the PRC. [ABSTRACT FROM AUTHOR]

The human oral structure contains organs with distinctly different physical properties, such as teeth, gums, and tongues. When food enters the oral cavity, we can recognize the tactile sensation and shape of the object from multiple perspectives through the texture of the teeth and tongue. Therefore, it is possible to regard oral structures as a group of tactile sensors based on these functions. In this study, we developed a soft-matter artificial mouth that can accurately detect subtle differences in texture by creating and combining oral structural organs using polymer materials with different physical properties and mounting them as end-effectors for a robot arm. The same piezoelectric film sensor was embedded inside each organ, making it possible to acquire tactile sensations from the same object as completely different signal waveforms. We tested whether the sensor data obtained from each soft-matter material could be used for excellent object recognition by applying various machine learning methods. In an actual experiment, we learned the waveform data obtained from chewing sweets and snacks, such as rice crackers, and applied machine learning to classify the data, which led to an accuracy rate of over 90%. [ABSTRACT FROM AUTHOR]

The rapid development in the field of artificial intelligence has increased the demand for neuromorphic computing hardware and its information‐processing capability. A spintronics device is a promising candidate for neuromorphic computing hardware and can be used in extreme environments due to its high resistance to radiation. Improving the information‐processing capability of neuromorphic computing is an important challenge for implementation. Herein, a novel neuromorphic computing framework using spintronics devices is proposed. This framework is called coupled spintronics reservoir (CSR) computing and exploits the high‐dimensional dynamics of coupled spin‐torque oscillators as a computational resource. The relationships among various bifurcations of the CSR and its information‐processing capabilities through numerical experiments are analyzed and it is found that certain configurations of the CSR boost the information‐processing capability of the spintronics reservoir toward or even beyond the standard level of machine learning networks. The effectiveness of our approach is demonstrated through conventional machine learning benchmarks and edge computing in real physical experiments using pneumatic artificial muscle‐based wearables, which assist human operations in various environments. This study significantly advances the availability of neuromorphic computing for practical uses. [ABSTRACT FROM AUTHOR]

We report tellurium (Te) thin-film-based artificial photonic synapses and their application to physical reservoir computing (PRC). The Te-based artificial photonic synapses were fabricated by using sputtered Te thin films and spray-coated MXene (Ti 3 C 2 ) electrodes. A thorough investigation of the field-dependent persistent photoconductivity (PPC) of the Te channel revealed that the relaxation speed of the transient photocurrent depended on the gate bias. Utilizing the PPC property, the Te device served as an excellent photonic synapse under light pulse stimulus, exhibiting multiple synaptic characteristics such as excitatory postsynaptic current and paired-pulse facilitation, as well as highly linear potentiation-depression characteristics; a simulation-based study further confirmed the effectiveness of the device. Most importantly, by exploiting the nonlinear and fading memory characteristics of the Te photonic synapse, we demonstrate two advanced examples of PRC. In classifying handwritten digits, our system carried out successful digit recognition without binarization or another simplification process with reduced computational cost compared to conventional systems. To solve second-order nonlinear equations, we introduce the strategy of utilizing historical nodes. The combination of historical nodes and the gate-tunable responses of the photonic synapses, which provide an enriched reservoir state, yielded excellent prediction accuracy. Overall, this work will offer an understanding of Te-based optoelectronic devices and their synergetic integration with neuromorphic devices and PRC.

Physical reservoir computing (PRC) using synaptic devices has attracted attention as a promising edge artificial intelligence device. To handle time-series data on various time scales, it is necessary to fabricate devices with the desired time scale. In this study, we fabricated a dye-sensitized solar-cell-based synaptic device with controllable time constants by changing the light intensity. This device showed synaptic features, such as paired-pulse facilitation and paired-pulse depression, in response to light intensity. Moreover, we found that the high computational performance of the time-series data processing task was achieved by changing the light intensity, even when the input pulse width was varied. In addition, the fabricated device can be used for motion recognition tasks. This study paves the way for realizing multiple time-scale PRC.

Bio-inspired Autonomous Underwater Vehicles with soft bodies provide significant performance benefits over conventional propeller-driven vehicles; however, it is difficult to control these vehicles due to their soft underactuated bodies. This study investigates the application of Physical Reservoir Computing (PRC) in the swimmer's flexible body to perform state estimation. This PRC informed state estimation has potential to be used in vehicle control. PRC is a type of recurrent neural network that leverages the nonlinear dynamics of a physical system to predict a nonlinear spatiotemporal input-output relationship. By embodying the neural network into the physical structure, PRC can process the response to an environment input with high computational efficiency. This study uses a soft bio-inspired propulsor embodied as a physical reservoir. We evaluate its ability to predict different state estimation tasks including hydrodynamic forces and benchmark computational tasks in response to the forcing applied to the artificial muscles during actuation. The propulsor's nonlinear fluid-structural dynamics act as the physical reservoir and the kinematic feedback serves as the reservoir readouts. We show that the bio-inspired underwater propulsor can predict the hydrodynamic thrust and benchmark tasks with high accuracy under specific input frequencies. By analyzing the frequency spectrum of the input, readouts, and target signals, we demonstrate that the system's dynamic response determines the frequency contents relevant to the task being predicted. The propulsor's ability to process information stems from its nonlinearity, as it is responsible to transform the input signal into a broader spectrum of frequency content at the readouts. This broad band of frequency content is necessary to recreate the target signal within the PRC algorithm, thereby improving the prediction performance. The spectral analysis provides a unique perspective to analyze the nonlinear dynamics of a physical reservoir and serves as a valuable tool for examining other types of vibratory systems for PRC. This work serves as a first step towards embodying computation into soft bio-inspired swimmers., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Patrick Musgrave reports a relationship with Air Force Office of Scientific Research that includes: funding grants. Patrick Musgrave reports a relationship with Air Force Research Laboratory that includes: funding grants and travel reimbursement. Shan He reports a relationship with Air Force Research Laboratory that includes: funding grants and travel reimbursement. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The rapid development in the field of artificial intelligence has increased the demand for neuromorphic computing hardware and its information‐processing capability. A spintronics device is a promising candidate for neuromorphic computing hardware and can be used in extreme environments due to its high resistance to radiation. Improving the information‐processing capability of neuromorphic computing is an important challenge for implementation. Herein, a novel neuromorphic computing framework using spintronics devices is proposed. This framework is called coupled spintronics reservoir (CSR) computing and exploits the high‐dimensional dynamics of coupled spin‐torque oscillators as a computational resource. The relationships among various bifurcations of the CSR and its information‐processing capabilities through numerical experiments are analyzed and it is found that certain configurations of the CSR boost the information‐processing capability of the spintronics reservoir toward or even beyond the standard level of machine learning networks. The effectiveness of our approach is demonstrated through conventional machine learning benchmarks and edge computing in real physical experiments using pneumatic artificial muscle‐based wearables, which assist human operations in various environments. This study significantly advances the availability of neuromorphic computing for practical uses.

More than 3.5 billion people live in rural areas, where water and water energy resources play an important role in ensuring sustainable and productive rural economies. This article reviews and critically analyses the recent advances in the field of analogue and reservoir computing that have been driven by the unique physical properties and energy of water waves. It also demonstrates that analogue and physical reservoir computing, taken as an independent research field, holds the potential to bring artificial intelligence closer to people living outside large cities, thus enabling them to enjoy the benefits of novel technologies that are already in place in large cities but are not readily available or suitable for regional communities. In particular, although the physical reservoir computing systems discussed in the main text are universal in terms of processing input data and making forecasts, they can be used to design and optimise power grid networks and forecast energy consumption, both at local and global scales. Thus, this review article will be of interest to a broad readership interested in novel concepts of artificial intelligence and machine learning and their innovative practical applications in diverse areas of science and technology.

The coronavirus epidemic has attracted significant attention to the applications of pet robots which can be used to treat and entertain people in their homes. However, pet robots are fabricated using hard materials and it is difficult for them to communicate with people through contact. Soft robots are expected to realize communication through contact similar to that of actual pets. Soft robots provide people with a sense of healing and security owing to their softness and can extract rich information through external stimuli by applying a machine learning framework called physical-reservoir computing. It is crucial to determine the differences between the physical properties of soft materials that affect the information extracted from a soft body to develop an intelligent soft robot. In this study, two owl-shaped soft robots with different softnesses were developed to analyze the characteristics of the signal data obtained via piezoelectric film sensors embedded in models with different physical properties. An accuracy of 94.2% and 95.9% was obtained for touched part classification using 1D CNN and logistic regression models, respectively. Additionally, the relationship between the softness of material and classification performance was investigated by comparing the distribution of part classification accuracy for different hyper-parameters of two owl models. [ABSTRACT FROM AUTHOR]

A McKibben-type pneumatic artificial muscle (PAM) is a soft actuator that is widely used in soft robotics, and it generally exhibits complex material dynamics with nonlinearity and hysteresis. In this letter, we propose an extremely durable PAM containing carbon black aggregates and show that its dynamics can be used as a computational resource based on the framework of physical reservoir computing (PRC). By monitoring the information processing capacity of our PAM, we verified that its computational performance will not degrade even if it is randomly actuated more than one million times, which indicates extreme durability. Furthermore, we demonstrate that the sensing function can be outsourced to the soft material dynamics itself without external sensors based on the framework of PRC. Our study paves the way toward reliable information processing powered by soft material dynamics. [ABSTRACT FROM AUTHOR]

Soft materials generate rich and diverse dynamics that can be used as computational resources based on the framework of physical reservoir computing. Herein, a method that exploits the dynamic coupling between soft structures and a water medium to allow for the transfer of computation to spatially distant locations is proposed. This technique is implemented by introducing the concept of remote reservoirs that can autonomously alter their physical constituents in real time rather than using reservoirs with predefined, fixed physical constituents. These remote reservoirs self‐organize by including many ad hoc physical substrates in the environment, making the framework more flexible for soft robotic applications. Using a simple experimental platform consisting of silicone rubber strips containing embedded flexible strain sensors, it is demonstrated that the dynamics of passive silicone rubber strips located at a distance from the actuation point can be successfully exploited for computation through generalized synchronization realized via the medium of water. Future application scenarios are illustrated, in which the proposed technique is applied in emergency situations as a communication tool for transferring a message to a location that humans cannot easily access. For example, the technique could be applied to communicate with people trapped in submerged caves. [ABSTRACT FROM AUTHOR]

This paper studies numerically how the signal detector arrangement influences the performance of reservoir computing using spin waves excited in a ferrimagnetic garnet film. This investigation is essentially important since the input information is not only conveyed but also transformed by the spin waves into high-dimensional information space when the waves propagate in the film in a spatially distributed manner. This spatiotemporal dynamics realizes a rich reservoir-computational functionality. First, we simulate spin waves in a rectangular garnet film with two input electrodes to obtain spatial distributions of the reservoir states in response to input signals, which are represented as spin vectors and used for a machine-learning waveform classification task. The detected reservoir states are combined through readout connection weights to generate a final output. We visualize the spatial distribution of the weights after training to discuss the number and positions of the output electrodes by arranging them at grid points, equiangularly circular points or at random. We evaluate the classification accuracy by changing the number of the output electrodes, and find that a high accuracy (>90%) is achieved with only several tens of output electrodes regardless of grid, circular or random arrangement. These results suggest that the spin waves possess sufficiently complex and rich dynamics for this type of tasks. Then we investigate in which area useful information is distributed more by arranging the electrodes locally on the chip. Finally, we show that this device has generalization ability for input wave-signal frequency in a certain frequency range. These results will lead to practical design of spin-wave reservoir devices for low-power intelligent computing in the near future.

Soft materials generate rich and diverse dynamics that can be used as computational resources based on the framework of physical reservoir computing. Herein, a method that exploits the dynamic coupling between soft structures and a water medium to allow for the transfer of computation to spatially distant locations is proposed. This technique is implemented by introducing the concept of remote reservoirs that can autonomously alter their physical constituents in real time rather than using reservoirs with predefined, fixed physical constituents. These remote reservoirs self‐organize by including many ad hoc physical substrates in the environment, making the framework more flexible for soft robotic applications. Using a simple experimental platform consisting of silicone rubber strips containing embedded flexible strain sensors, it is demonstrated that the dynamics of passive silicone rubber strips located at a distance from the actuation point can be successfully exploited for computation through generalized synchronization realized via the medium of water. Future application scenarios are illustrated, in which the proposed technique is applied in emergency situations as a communication tool for transferring a message to a location that humans cannot easily access. For example, the technique could be applied to communicate with people trapped in submerged caves.

Acoustic keyword spotting (KWS) plays a pivotal role in the voice-activated systems of artificial intelligence (AI), allowing for hands-free interactions between humans and smart devices through information retrieval of the voice commands. The cloud computing technology integrated with the artificial neural networks has been employed to execute the KWS tasks, which however suffers from propagation delay and the risk of privacy breach. Here, we report a single-node reservoir computing (RC) system based on the CuInP 2 S 6 (CIPS)/graphene heterostructure planar device for implementing the KWS task with low computation cost. Through deliberately tuning the Schottky barrier height at the ferroelectric CIPS interfaces for the thermionic injection and transport of the electrons, the typical nonlinear current response and fading memory characteristics are achieved in the device. Additionally, the device exhibits diverse synaptic plasticity with an excellent separation capability of the temporal information. We construct a RC system through employing the ferroelectric device as the physical node to spot the acoustic keywords, i.e., the natural numbers from 1 to 9 based on simulation, in which the system demonstrates outstanding performance with high accuracy rate (>94.6%) and recall rate (>92.0%). Our work promises physical RC in single-node configuration as a prospective computing platform to process the acoustic keywords, promoting its applications in the artificial auditory system at the edge.

Sensing and computing the body states of soft-bodied robots require new methods. Recent studies on soft robotics have shown their feasibility to be applied for these purposes; however, they only addressed solid parts and not the behavior of inner fluids in soft robotics. In this study, we investigated the possibility of a framework that can be used to estimate the body state by exploiting air dynamics in tubes connecting chambers. The framework was designed on the basis of the concept of physical reservoir computing. We focused on a case with a single tube connection. A benchmark task emulated a nonlinear system and was evaluated by simulation. The results showed that the computational ability depended on the inner diameter and length of the tube and can be increased by selecting a suitable diameter and length. We physically implemented the framework for the posture estimation of a soft exoskeleton using pneumatic artificial rubber muscles (PARMs) as the connected chambers and evaluated the accuracy of estimation of a thigh angle. The estimation accuracy showed a similar trend as a function of the tube properties as that observed in the simulation. The framework can exploit the dynamics of air in a tube and may be useful for the state estimation of soft-bodied robots. [ABSTRACT FROM AUTHOR]

Flapping‐wing unmanned aerial vehicles have potential advantages, such as consuming lower energy by leveraging the force of wind. Since the flapping movements of the soft wings contain information about the wind, measuring the movement of each part of the wings allows these vehicles to distinguish the direction of the wind. To confirm this prediction, herein, the detection of wind flow from the flapping‐wing motion of a bird robot using an integrated flexible strain sensor on its wing and a physical reservoir computing analysis is presented. In the presence of different wind directions, the movement of the flapping‐wings is measured using flexible strain sensors, and the current wind direction is detected by capitalizing on the intrinsic wing dynamics. As a result, it is found that the detection accuracy using our embedded flexible strain sensors is significantly high, showing a similar level of accuracy with a high‐speed camera recorded from the fixed position in the environment. The results indicate that flapping‐wing unmanned aerial vehicles can recognize wind direction by exploiting the natural dynamics of their wings.

Flapping‐wing unmanned aerial vehicles have potential advantages, such as consuming lower energy by leveraging the force of wind. Since the flapping movements of the soft wings contain information about the wind, measuring the movement of each part of the wings allows these vehicles to distinguish the direction of the wind. To confirm this prediction, herein, the detection of wind flow from the flapping‐wing motion of a bird robot using an integrated flexible strain sensor on its wing and a physical reservoir computing analysis is presented. In the presence of different wind directions, the movement of the flapping‐wings is measured using flexible strain sensors, and the current wind direction is detected by capitalizing on the intrinsic wing dynamics. As a result, it is found that the detection accuracy using our embedded flexible strain sensors is significantly high, showing a similar level of accuracy with a high‐speed camera recorded from the fixed position in the environment. The results indicate that flapping‐wing unmanned aerial vehicles can recognize wind direction by exploiting the natural dynamics of their wings. [ABSTRACT FROM AUTHOR]

This manuscript serves a specific purpose: to give readers from fields such as material science, chemistry, or electronics an overview of implementing a reservoir computing (RC) experiment with her/his material system. Introductory literature on the topic is rare and the vast majority of reviews puts forth the basics of RC taking for granted concepts that may be nontrivial to someone unfamiliar with the machine learning field (see for example reference Lukoševičius (2012 Neural Networks: Tricks of the Trade (Berlin: Springer) pp 659–686). This is unfortunate considering the large pool of material systems that show nonlinear behavior and short-term memory that may be harnessed to design novel computational paradigms. RC offers a framework for computing with material systems that circumvents typical problems that arise when implementing traditional, fully fledged feedforward neural networks on hardware, such as minimal device-to-device variability and control over each unit/neuron and connection. Instead, one can use a random, untrained reservoir where only the output layer is optimized, for example, with linear regression. In the following, we will highlight the potential of RC for hardware-based neural networks, the advantages over more traditional approaches, and the obstacles to overcome for their implementation. Preparing a high-dimensional nonlinear system as a well-performing reservoir for a specific task is not as easy as it seems at first sight. We hope this tutorial will lower the barrier for scientists attempting to exploit their nonlinear systems for computational tasks typically carried out in the fields of machine learning and artificial intelligence. A simulation tool to accompany this paper is available online ^7 .

We propose a reservoir computing device utilizing spin waves that propagate in a garnet film equipped with multiple input/output electrodes. In recent years, reservoir computing has been expected to realize energy-efficient and/or high-speed machine learning. Our proposed device enhances such significant merits in a hardware approach. It utilizes the nonlinear interference of history-dependent asymmetrically propagating spin waves excited by the magneto-electric effect. First, we investigate a feasible device structure with practical physical parameters in micromagnetic numerical analysis, and show the detailed characteristics of the forward volume magnetostatic spin waves. Then, we demonstrate high generalization ability in the estimation of input-signal parameters performed by the spin-wave-based reservoir computing. We find that the hysteresis characteristics of the spin waves propagating asymmetrically with respect to excitation points, as well as the nonlinear interference, works advantageously to realize high diversity in the time-sequential signals in high-dimensional information space, which has the highest significance for effective learning in reservoir computing. The spin wave device is highly promising for next-generation machine-learning electronics.

Fluids exist universally in nature and technology. Among the many types of fluid flows is the well-known vortex shedding, which takes place when a fluid flows past a bluff body. Diverse types of vortices can be found in this flow as the Reynolds number increases. In this study, we reveal that these vortices can be employed for conducting certain types of computation. The results from computational fluid dynamics simulations showed that optimal computational performance is achieved near the critical Reynolds number, where the flow exhibits a twin vortex before the onset of the Kármán vortex shedding associated with the Hopf bifurcation. It is revealed that as the Reynolds number increases toward the bifurcation point, the input sensitivity of the twin vortex motion also increases, suggesting the modality of information processing within the system. Our finding paves a novel path to understand the relationship between fluid dynamics and its computational capability.

Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength. On the one hand, the use of flexible materials can lead to higher power efficiency and more fluent and robust motions. On the other hand, using embodiment in a closed-loop controller, part of the control task itself can be outsourced to the body dynamics. This can significantly simplify the additional resources required for locomotion control. To this goal, a first step consists in an exploration of the trade-offs between morphology, efficiency of locomotion, and the ability of a mechanical body to serve as a computational resource. In this work, we use a detailed dynamical model of a Mass–Spring–Damper (MSD) network to study these trade-offs. We first investigate the influence of the network size and compliance on locomotion quality and energy efficiency by optimizing an external open-loop controller using evolutionary algorithms. We find that larger networks can lead to more stable gaits and that the system’s optimal compliance to maximize the traveled distance is directly linked to the desired frequency of locomotion. In the last set of experiments, the suitability of MSD bodies for being used in a closed loop is also investigated. Since maximally efficient actuator signals are clearly related to the natural body dynamics, in a sense, the body is tailored for the task of contributing to its own control. Using the same simulation platform, we therefore study how the network states can be successfully used to create a feedback signal and how its accuracy is linked to the body size. [ABSTRACT FROM AUTHOR]