A storage-efficient SNN–CNN hybrid network with RRAM-implemented weights for traffic signs recognition

Zhang, Yufei; Xu, Hui; Huang, Lixing; Chen, Changlin

doi:10.1016/j.engappai.2023.106232

Cited by 9 publications

(3 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To enhance the storage and computing efficiency of TSR, the authors propose a hybrid SNN-CNN network with weights implemented in RRAM 42 . Comparing the SNN-CNN hybrid network with state-of-the-art CNN methods, the hybrid network achieves similar accuracy with 69.21% less weighted parameters and 81.55% lower power consumption.…”

Section: Related Workmentioning

confidence: 99%

An efficient intrusion detection model based on convolutional spiking neural network

Wang,

Ghaleb,

Zainal

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Many intrusion detection techniques have been developed to ensure that the target system can function properly under the established rules. With the booming Internet of Things (IoT) applications, the resource-constrained nature of its devices makes it urgent to explore lightweight and high-performance intrusion detection models. Recent years have seen a particularly active application of deep learning (DL) techniques. The spiking neural network (SNN), a type of artificial intelligence that is associated with sparse computations and inherent temporal dynamics, has been viewed as a potential candidate for the next generation of DL. It should be noted, however, that current research into SNNs has largely focused on scenarios where limited computational resources and insufficient power sources are not considered. Consequently, even state-of-the-art SNN solutions tend to be inefficient. In this paper, a lightweight and effective detection model is proposed. With the help of rational algorithm design, the model integrates the advantages of SNNs as well as convolutional neural networks (CNNs). In addition to reducing resource usage, it maintains a high level of classification accuracy. The proposed model was evaluated against some current state-of-the-art models using a comprehensive set of metrics. Based on the experimental results, the model demonstrated improved adaptability to environments with limited computational resources and energy sources.

show abstract

Section: Related Workmentioning

confidence: 99%

An efficient intrusion detection model based on convolutional spiking neural network

Wang,

Ghaleb,

Zainal

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…When converted into SNNs mimicking the biological brain's transmission system, vectors are transformed into numbers of spikes depending on the vector's amplitude. [60] The amplitude of the spikes is determined as the RRAM cell's read voltage, and the sum of the currents that result from spikes simultaneously applied to each word line according to the timestep forms the result of the VMM.…”

Section: Vector-matrix Multiplication Measurementmentioning

confidence: 99%

Synaptic Characteristics and Vector‐Matrix Multiplication Operation in Highly Uniform and Cost‐Effective Four‐Layer Vertical RRAM Array

Kim,

Lee,

Kim

et al. 2023

Adv Funct Materials

View full text Add to dashboard Cite

This study implements a highly uniform 3D vertically stack resistive random‐access memory (VRRAM) with a four‐layer contact hole structure. The fabrication process of a four‐layer VRRAM is demonstrated, and its physical and electrical properties are thoroughly examined. X‐ray photoelectron spectroscopy and transmission electron microscopy are employed to analyze the chemical distribution and physical structure of the VRRAM device. Multilevel capability, reliable endurance (>104 cycles), and retention (104 s) are successfully obtained. Synaptic memory plasticity, such as spike time‐dependent plasticity, spike rate‐dependent plasticity, excitatory post‐synaptic current, paired‐pulse facilitation, and long‐term potentiation and depression is presented. Finally, the vector‐matrix multiplication (VMM) operation is conducted on a 4 × 12 VRRAM array, according to the low resistance state ratio. It is ascertained that the accuracy drop, which can occur due to VMM error, can be limited to a decrease of less than 0.44% point. Utilizing the high‐density, multilevel, and biological characteristics of VRRAM, it is possible to implement high‐performance neuromorphic systems that require densely integrated synaptic devices.

show abstract

“…Background information is frequently ignored, but some researchers have improved model accuracy by using background detail features of neighboring signs [37,38]. In addition, spiking neural networks (SNN) are used to improve existing traffic sign detection and recognition algorithms [39][40][41], which can extract time-related features and have higher computational efficiency on hardware platforms.…”

Section: Introductionmentioning

confidence: 99%

Traffic Sign Detection and Recognition Using Multi-Frame Embedding of Video-Log Images

Huang

Ying

2023

Remote Sensing

View full text Add to dashboard Cite

The detection and recognition of traffic signs is an essential component of intelligent vehicle perception systems, which use on-board cameras to sense traffic sign information. Unfortunately, issues such as long-tailed distribution, occlusion, and deformation greatly decrease the detector’s performance. In this research, YOLOv5 is used as a single classification detector for traffic sign localization. Afterwards, we propose a hierarchical classification model (HCM) for the specific classification, which significantly reduces the degree of imbalance between classes without changing the sample size. To cope with the shortcomings of a single image, a training-free multi-frame information integration module (MIM) was constructed, which can extract the detection sequence of traffic signs based on the embedding generated by the HCM. The extracted temporal detection information is used for the redefinition of categories and confidence. At last, this research performed detection and recognition of the full class on two publicly available datasets, TT100K and ONCE. Experimental results show that the HCM-improved YOLOv5 has a mAP of 79.0 in full classes, which exceeds that of state-of-the-art methods, and achieves an inference speed of 22.7 FPS. In addition, MIM further improves model performance by integrating multi-frame information while only slightly increasing computational resource consumption.

show abstract

A storage-efficient SNN–CNN hybrid network with RRAM-implemented weights for traffic signs recognition

Cited by 9 publications

References 46 publications

An efficient intrusion detection model based on convolutional spiking neural network

An efficient intrusion detection model based on convolutional spiking neural network

Synaptic Characteristics and Vector‐Matrix Multiplication Operation in Highly Uniform and Cost‐Effective Four‐Layer Vertical RRAM Array

Traffic Sign Detection and Recognition Using Multi-Frame Embedding of Video-Log Images

Contact Info

Product

Resources

About