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The recent emergence of various smart wearable electronics has furnished the rapid development of human–computer interaction, medical health monitoring technologies, etc. Unfortunately, processing redundant motion and physiological data acquired by multiple wearable sensors using conventional off‐site digital computers typically result in serious latency and energy consumption problems. In this work, a multi‐gate electrolyte‐gated transistor (EGT)‐based reservoir device for efficient multi‐channel near‐sensor computing is reported. The EGT, exhibiting rich short‐term dynamics under voltage modulation, can implement nonlinear parallel integration of the time‐series signals thus extracting the temporal features such as the synchronization state and collective frequency in the inputs. The flexible EGT integrated with pressure sensors can perform on‐site gait information analysis, enabling the identification of motion behaviors and Parkinson's disease. This near‐sensor reservoir computing system offers a new route for rapid analysis of the motion and physiological signals with significantly improved efficiency and will lead to robust smart flexible wearable electronics. This work reports a multi‐gate electrolyte‐gated transitior (EGT)‐based reservoir device capable of parallel integrating and processing multi‐channel streaming signals. This reservoir can be integrated with multiple pressure sensors to extract the temporal features including the synchronization state and collective frequency in the sensory inputs. Accurate identification of gait patterns during bipedal movement is realized, providing new ideas for smart wearable electronics.

To efficiently process the massive amount of sensor data, it is demanding to develop a new paradigm. Inspired by neurobiological systems, an infrared near‐senor reservoir computing (RC) system, consisting of infrared sensors and memristors based on single‐crystalline LiTaO3 and LiNbO3 (LN) thin film respectively, is demonstrated. The analog memristor is used as a reservoir in the RC system to process sensor signals with spatiotemporal characteristics. LN crystal structure stacked with oxygen octahedra provides favorable conditions for reliable Mott variable‐range hopping conduction, which provides the memristor with tens of thousands of reservoir states within a large dynamic range. With the characteristics, the analog sensor signals with high data fidelity can be directly fed to the memristive reservoir, and the spatiotemporal features can be separated and mapped. The system demonstrated a dynamic gesture perception task, achieving an accuracy of 99.6%, which highlights the great application potential of the memristor in signal sensor processing and will advance the application of artificial intelligence in sensor systems. Crystal ion slicing techniques are used to fabricate a single‐crystalline thin film for both the memristor and sensor, which opens up the possibility of realizing monolithic integration of a memristor‐based near‐sensor computing system. A novel infrared near‐sensor reservoir computing (RC) system constructed from an ion‐slicing LiTaO3‐based infrared array and ion‐slicing LiNbO3‐based memristor array is demonstrated. Thanks to the excellent capacities of the memristor reservoir, the infrared near‐senor RC system successfully and robustly implemented a dynamic gesture perception task with spatiotemporal feature fusion.

Artificial olfactory systems (AOSs) that mimic biological olfactory systems are of great interest. However, most existing AOSs suffer from high energy consumption levels and latency issues due to data conversion and transmission. In this work, an energy‐ and area‐efficient AOS based on near‐sensor computing is proposed. The AOS efficiently integrates an array of sensing units (merged field effect transistor (FET)‐type gas sensors and amplifier circuits) and an AND‐type nonvolatile memory (NVM) array. The signals of the sensing units are directly connected to the NVM array and are computed in memory, and the meaningful linear combinations of signals are output as bit line currents. The AOS is designed to detect food spoilage by employing thin zinc oxide films as gas‐sensing materials, and it exhibits low detection limits for H 2 S and NH 3 gases (0.01 ppm), which are high‐protein food spoilage markers. As a proof of concept, monitoring the entire spoilage process of chicken tenderloin is demonstrated. The system can continuously track freshness scores and food conditions throughout the spoilage process. The proposed AOS platform is applicable to various applications due to its ability to change the sensing temperature and programmable NVM cells.

As the demand for the neuromorphic vision system in image recognition experiences rapid growth, it is imperative to develop advanced architectures capable of processing perceived data proximal to sensory terminals. This approach aims to reduce data movement between sensory and computing units, minimizing the need for data transfer and conversion at the sensor‐processor interface. Here, an optical neuromorphic synaptic (ONS) device is demonstrated by homogeneously integrating optical‐sensing and synaptic functionalities into a unified material platform, constructed exclusively by all‐inorganic perovskite CsPbBr3 quantum dots (QDs). The dual functionality of each unit within the ONS device, which can be operated as either an optical sensor or a synaptic device depending on applied electrical polarity, provides significant advantages over previous heterogeneous integration methods, particularly regarding material selection, structural compatibility, and device fabrication complexity. The ONS device exhibits distinct wavelength responses essential for emulating colored image recognition capability inherent in the human visual system. Additionally, the seamless integration of electronics and photonics within a unified material system establishes a novel paradigm for optical retrieval, enabling real‐time perception of the encoded status of the ONS device. These findings represent substantial advancements in near‐sensor computing platforms and open a new horizon for all‐inorganic perovskite optoelectronic technologies. This work demonstrates an optical neuromorphic synaptic (ONS) device by homogeneously integrating optical‐sensing and synaptic functionalities into a unified material platform, constructed exclusively by all‐inorganic perovskite CsPbBr3 quantum dots, to emulate the colored image recognition capabilities of the human visual system.

HighlightsThe neuromorphic vision sensors for near-sensor and in-sensor computing of visual information are implemented using optoelectronic synaptic circuits and single-device optoelectronic synapses, respectively.This review focuses on the recent progress, working mechanisms, and image pre-processing techniques about two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.

The rotary motor plays a pivotal role in various motion execution mechanisms. However, an inherent issue arises during the initial installation of the encoder grating, namely, eccentricity between the centers of the encoder grating and motor shaft. This eccentricity substantially affects the accuracy of motor angle measurements. To address this challenge, we proposed a precision encoder grating mounting system that automates the encoder grating mounting process. The proposed system mainly comprises a near-sensor detector and a push rod. With the use of a near-sensor approach, the detector captures rotating encoder grating images, and the eccentricity is computed in real-time. This approach substantially reduces the time delays in image data transmission, thereby enhancing the speed and accuracy of eccentricity calculation. The major contribution of this article is a method for real-time eccentricity calculation that leverages an edge processor within the detector and an edge-vision baseline detection algorithm. This method enables real-time determination of the eccentricity and eccentricity angle of the encoder grating. Leveraging the obtained eccentricity and eccentricity angle data, the position of the encoder grating can be automatically adjusted by the push rod. In the experimental results, the detector can obtain the eccentricity and eccentricity angle of the encoder grating within 2.8 s. The system efficiently and precisely completes a encoder grating mounting task in average 25.1 s, and the average eccentricity after encoder grating mounting is 3.8 µm.

Today, a wide range of developments and paradigms require the use of embedded systems characterized by restrictions on their computing capacity, consumption, cost, and network connection. The evolution of the Internet of Things (IoT) towards Industrial IoT (IIoT) or the Internet of Multimedia Things (IoMT), its impact within the 4.0 industry, the evolution of cloud computing towards edge or fog computing, also called near-sensor computing, or the increase in the use of embedded vision, are current examples of this trend. One of the most common methods of reducing energy consumption is the use of processor frequency scaling, based on a particular policy. The algorithms to define this policy are intended to obtain good responses to the workloads that occur in smarthphones. There has been no study that allows a correct definition of these algorithms for workloads such as those expected in the above scenarios. This paper presents a method to determine the operating parameters of the dynamic governor algorithm called Interactive, which offers significant improvements in power consumption, without reducing the performance of the application. These improvements depend on the load that the system has to support, so the results are evaluated against three different loads, from higher to lower, showing improvements ranging from 62% to 26%.

As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people’s daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant data and communication delays due to the use of a large number of sensors. Fortunately, the emerging paradigms of near-sensor and in-sensor computing, together with the proposal of flexible neuromorphic devices, provides a viable solution for the application of intelligent low-power wearable devices. Therefore, wearable smart systems based on new computing paradigms are of great research value. This review discusses the research status of a flexible five-sense sensing system based on near-sensor and in-sensor architectures, considering material design, structural design and circuit design. Furthermore, we summarize challenging problems that need to be solved and provide an outlook on the potential applications of intelligent wearable devices.

In-memory computing (IMC) represents a promising approach to reducing latency and enhancing the energy efficiency of operations required for calculating convolution products of images. This study proposes a fully differential current-mode architecture for computing image convolutions across all four quadrants, intended for deep learning applications within CMOS imagers utilizing IMC near the CMOS sensor. This architecture processes analog signals provided by a CMOS sensor without the need for analog-to-digital conversion. Furthermore, it eliminates the necessity for data transfer between memory and analog operators as convolutions are computed within modified SRAM memory. The paper suggests modifying the structure of a CMOS SRAM cell by incorporating transistors capable of performing multiplications between binary (−1 or +1) weights and analog signals. Modified SRAM cells can be interconnected to sum the multiplication results obtained from individual cells. This approach facilitates connecting current inputs to different SRAM cells, offering highly scalable and parallelized calculations. For this study, a configurable module comprising nine modified SRAM cells with peripheral circuitry has been designed to calculate the convolution product on each pixel of an image using a 3 × 3 mask with binary values (−1 or 1). Subsequently, an IMC module has been designed to perform 16 convolution operations in parallel, with input currents shared among the 16 modules. This configuration enables the computation of 16 convolutions simultaneously, processing a column per cycle. A digital control circuit manages both the readout or memorization of digital weights, as well as the multiply and add operations in real-time. The architecture underwent testing by performing convolutions between binary masks of 3 × 3 values and images of 32 × 32 pixels to assess accuracy and scalability when two IMC modules are vertically integrated. Convolution weights are stored locally as 1-bit digital values. The circuit was synthesized in 180 nm CMOS technology, and simulation results indicate its capability to perform a complete convolution in 3.2 ms, achieving an efficiency of 11,522 1-b TOPS/W (1-b tera-operations per second per watt) with a similarity to ideal processing of 96%.