Regular pattern matching is essential for applications such as text processing, malware detection, network security, and bioinformatics.Recent in-memory automata processors have significantly advanced the energy and memory efficiency over conventional computing platforms.However, these processors are typically optimized only for one type of automata, limiting their capability to efficiently support regex processing under diverse real-world workloads.This paper presents RAP, the first reconfigurable in-memory automata processor for efficient regular pattern matching across diverse workloads.It supports Nondeterministic Finite Automata (NFA), Nondeterministic Bit Vector Automata (NBVA), and Linear NFA (LNFA) through reconfigurable architecture and circuit designs, and a compiler for translation.RAP is evaluated in 28nm CMOS PDK, achieving 1.2-1.5×higher energy efficiency and 1.3-2.5×higher compute density compared to SotA automata processors for NFA (CA and CAMA) over diverse real-world benchmarks.It also achieves 1.6× higher compute density and similar energy efficiency as BVAP, a SotA optimized for bounded repetitions.Finally, RAP is >100× and >1000× more energy efficient than SotA GPU and CPU solutions.

Regular pattern matching is pervasive in applications such as text processing, malware detection, network security, and bioinformatics. Recent studies have demonstrated specialized in-memory automata processors with superior energy and memory efficiencies than existing computing platforms. Yet, they lack efficient support for the construct of bounded repetition that is widely used in regular expressions (regexes). This paper presents BVAP, a software-hardware co-designed in-memory Bit Vector Automata Processor. It is enabled by a novel theoretical model called Action-Homogeneous Non-deterministic Bit Vector Automata (AH-NBVA), its efficient hardware implementation, and a compiler that translates regexes into hardware configurations. BVAP is evaluated with a cycle-accurate simulator in a 28nm CMOS process, achieving 67-95% higher energy efficiency and 42-68% lower area, compared to state-of-the-art automata processors (CA, eAP, and CAMA), across a set of real-world benchmarks.

Securing low-power Internet-of- Things (IoT) sensor nodes presents a critical challenge for the widespread adoption of IoT technology, given their inherent limitations in energy, computation, and storage resources. As a promising alternative to conventional wireless security approaches based on cryptography, there has been a growing interest in RF physical-layer security, especially RF fingerprinting, which offers the promise of reduced overhead and energy consumption. In this work, we present an artificial neural network (ANN) model tailored to identify IoT transmitters by harnessing their unique power spectral density (PSD). The network is designed to be lightweight and can be readily implemented on resource-constrained IoT nodes. Combined with our customized radio frontend, we achieve superior identification performance. In the measurements, we can reliably identify 240 devices with a 99 % accuracy on trained distances and 40 devices with an above 95 % accuracy at an unknown distance that is excluded from the training data. These results demonstrate significant improvement in robustness, reliability, and identification accuracy over prior art while ensuring compatibility with resource-constrained IoT nodes.

In this paper, we propose SYGNet to strengthen the scene parsing ability of autonomous driving under complicated road conditions. The SYGNet includes feature extraction component and SVD-YOLO GhostNet component. The SVD-YOLO GhostNet component combines Singular Value Decomposition (SVD), You Only Look Once (YOLO) and GhostNet. In the feature extraction component, we propose an algorithm based on VoxelNet to extract point cloud features and image features. In SVD-YOLO GhostNet component, the image data is decomposed by SVD, and we obtain data with stronger spatial and environmental characteristics. YOLOv3 is used to obtain the future map, then convert to GhostNet, which is used to realize the real-time scene parsing. We use KITTI data set to perform our experiments and the results show that the SYGNet is more robust and can further enhance the accuracy of real-time driving scene parsing. The model code, data set, and results of the experiments in this paper are available at: https://github.com/WangHewei16/SYGNet-for-Real-time-Driving-Scene-Parsing.

This paper proposes an energy-efficient intelligent pulmonary auscultation system for post COVID-19 era wearable monitoring. This system consists of a tightly coupled two-stage hybrid neural network (TC-TSHNN) model and a corresponding multi-task training paradigm to improve prediction accuracy and generalization ability based on the fact that the number of COVID-19 patients is far less than that of normal people. At the first stage, two-category coarse classification is performed to identify normal and abnormal lung sounds. If the lung sound is abnormal, the second stage would be triggered to perform a four-category fine-grained classification. Besides, discrete wavelet transform is utilized for feature extraction, denoising and data reduction. In addition, advanced lightweight convolutional neural networks are used to reduce the model’s computation and improve the model’s performance. The hybrid network model can achieve 92% computation reduction and energy saving compared with a direct four-category classification when the input lung sound is normal, which is the majority of cases. Experiment results with inter-patient classification on the COVID-19 lung sound dataset from Tongji Hospital in Wuhan City and the ICBHI’17 dataset show that the proposed TC-TSHNN model can significantly reduce power consumption while maintaining competitive performance against the state-of-the-art work.