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The Reservoir Computing, a neural computing framework suited for temporal information processing, utilizes a dynamic reservoir layer for high-dimensional encoding, enhancing the separability of the network. In this paper, we exploit a Deep Learning (DL)-based detection strategy for Multiple-input, Multiple-output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) symbol detection. To be specific, we introduce a Deep Echo State Network (DESN), a unique hierarchical processing structure with multiple time intervals, to enhance the memory capacity and accelerate the detection efficiency. The resulting hardware prototype with the hybrid memristor-CMOS co-design provides in-memory computing and parallel processing capabilities, significantly reducing the hardware and power overhead. With the standard 180nm CMOS process and memristive synapses, the introduced DESN consumes merely 105mW of power consumption, exhibiting 16.7% power reduction compared to shallow ESN designs even with more dynamic layers and associated neurons. Furthermore, numerical evaluations demonstrate the advantages of the DESN over state-of-the-art detection techniques in the literate for MIMO-OFDM systems even with a very limited training set, yielding a 47.8% improvement against conventional symbol detection techniques.

With the continuous development of technologies, our society is approaching the next stage of industrialization. The Fourth Industrial Revolution also referred to as Industry 4.0, redefines the manufacturing system as a smart and connected machinery system with fully autonomous operation capability. Several advanced cutting-edge technologies, such as cyber-physical systems (CPS), the internet of things (IoT), and artificial intelligence, are believed to the essential components to realize Industry 4.0. In this paper, we focus on a comprehensive review of how artificial intelligence benefits Industry 4.0, including potential challenges and possible solutions. A panoramic introduction of neuromorphic computing is provided, which is one of the most promising and attractive research directions in artificial intelligence. Subsequently, we introduce the vista of the neuromorphic-powered Industry 4.0 system and survey a few research activities on applications of artificial neural networks for IoT.

The delay feedback reservoir, as a branch of reservoir computing, has attracted a wide range of research interests because of its training efficiency and its simplicity for hardware implementation. However, its potential for processing various kinds of data, like sequential and matrix data, has not been fully explored. In this paper, we present a unified information processing structure by fusing the convolutional or fully connected neural network with the delay feedback reservoir into a hybrid neural network model to accomplish the comprehensive information processing goal. Our experimental results show that our methodology achieves high accuracy in both image classification and speech recognition, yielding 99.03% testing accuracy on the handwritten digits dataset (MNIST) and 97.3% on Spoken Digits Command Dataset (SDCD).

Deep Neural Networks (DNNs), a brain-inspired learning methodology, requires tremendous data for training before performing inference tasks. The recent studies demonstrate a strong positive correlation between the inference accuracy and the size of the DNNs and datasets, which leads to an inevitable demand for large DNNs. However, conventional memory techniques are not adequate to deal with the drastic growth of dataset and neural network size. Recently, a resistive memristor has been widely considered as the next generation memory device owing to its high density and low power consumption. Nevertheless, its high switching resistance variations (cycle-tocycle) restrict its feasibility in deep learning. In this work, a novel memristor configuration with the enhanced heat dissipation feature is fabricated and evaluated to address this challenge. Our experimental results demonstrate our memristor reduces the resistance variation by 30% and the inference accuracy increases correspondingly in a similar range. The accuracy increment is evaluated by our Deep Delay-feed-back (Deep-DFR) reservoir computing model. The design area, power consumption, and latency are reduced by 48%, 42%, and 67%, respectively, compared to the conventional SRAM memory technique (6T). The performance of our memristor is improved at various degrees ( 13%-73%) compared to the state-of-the-art memristors.