In this paper, we introduce a deep spiking delayed feedback reservoir (DFR) model to combine DFR with spiking neuros: DFRs are a new type of recurrent neural networks (RNNs) that are able to capture the temporal correlations in time series while spiking neurons are energy-efficient and biologically plausible neurons models. The introduced deep spiking DFR model is energy-efficient and has the capability of analyzing time series signals. The corresponding field programmable gate arrays (FPGA)-based hardware implementation of such deep spiking DFR model is introduced and the underlying energy-efficiency and recourse utilization are evaluated. Various spike encoding schemes are explored and the optimal spike encoding scheme to analyze the time series has been identified. To be specific, we evaluate the performance of the introduced model using the spectrum occupancy time series data in MIMO-OFDM based cognitive radio (CR) in dynamic spectrum sharing (DSS) networks. In a MIMO-OFDM DSS system, available spectrum is very scarce and efficient utilization of spectrum is very essential. To improve the spectrum efficiency, the first step is to identify the frequency bands that are not utilized by the existing users so that a secondary user (SU) can use them for transmission. Due to the channel correlation as well as users' activities, there is a significant temporal correlation in the spectrum occupancy behavior of the frequency bands in different time slots. The introduced deep spiking DFR model is used to capture the temporal correlation of the spectrum occupancy time series and predict the idle/busy subcarriers in future time slots for potential spectrum access. Evaluation results suggest that our introduced model achieves higher area under curve (AUC) in the receiver operating characteristic (ROC) curve compared with the traditional energy detection-based strategies and the learning-based support vector machines (SVMs).

The reservoir computing, an emerging computing paradigm, has proven its benefit to multifarious applications. In this work, we successfully designed and fabricated an analog delayed feedback reservoir (DFR) chip. Measurement results demonstrate its rich dynamic behaviors and high energy efficiency. System performance, as well as the robustness, are evaluated. The application of video frame recognition is investigated using a hybrid neural network, which employs the multilayer perceptron (MLP) training model as the readout layer of our designed DFR system, and yields 98% classification accuracy. Compared to results of using the MLP training only, our hybrid training model exhibits much higher recognition rate and accuracy.

A new method for attack detection of smart grids with wind power generators using reservoir computing (RC) is introduced in this paper. RC is an energy-efficient computing paradigm within the field of neuromorphic computing and the delayed feedback networks (DFNs) implementation of RC has shown superior performance in many classification tasks. The combination of temporal encoding, DFN, and a multilayer perceptron (MLP) as the output readout layer is shown to yield performance improvement over existing attack detection methods such as MLPs, support vector machines (SVM), and conventional state vector estimation (SVE) in terms of attack detection in smart grids. The proposed algorithms are shown to be more robust than MLP and SVE in dealing with different variables such as the amplitude of the attack, attack types, and the number of compromised measurements in smart grids. The attack detection rate for the proposed RC-based system is higher than 99%, based on the accuracy metric for the average of 10 000 simulations.

As the novel paradigm in the field of machine learning, reservoir computing possesses exceptional performance, e.g., energy efficiency, in tasks in which the traditional von Neumann computing systems cannot incorporate. This makes reservoir computing an ideal candidate to enable the sustainable development of cyber-physical systems (CPS). In the realm of CPS, the tight interaction among physical objects places security threats under the spotlight of attention. For such systems, especially the power grid network, false data injection could potentially lead to catastrophic consequences such as blackouts in large geographical areas. In this paper, we will introduce a reservoir computing architecture, the delayed feedback system, and apply the reservoir computing architecture for anomaly detection. To be specific, detailed design of the three imperative components in the delayed feedback system will be discussed and the corresponding energy efficiency performance will be analyzed. The application of the reservoir computing architecture to anomaly detection in a smart grid network will be introduced.

The rate of enhancement is starting to saturate and slow down which indicates the end of Moore's prediction due to the fundamental performance limits of the chips. The need of breaking through the barrier has directed researchers into several directions, for instance, novel computing architecture. Reservoir computing, a novel concept in the field of machine learning, has emerged over the past few years. Combined the memory and spatio-temporal processing of recurrent neural networks, reservoir computing possesses the capability of processing temporal information. In this paper, we present an analog hardware implementation of delayed feedback reservoir computing system. We build a new class of computationally efficient spike timing-dependent encoders and delay-based reservoirs within reservoir networks. This approach allows us to avoid using power-consuming analog-to-digital converters (ADCs) and operational amplifiers (Op-AMPs), resulting in significant savings in power requirements and design area.