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Artykuły w czasopismach na temat „Binary neural networks (BNN)”

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Data publikacji: 19 lipca 2023

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1

Rozen, Tal, Moshe Kimhi, Brian Chmiel, Avi Mendelson, and Chaim Baskin. "Bimodal-Distributed Binarized Neural Networks." Mathematics 10, no. 21 (2022): 4107. http://dx.doi.org/10.3390/math10214107.

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Binary neural networks (BNNs) are an extremely promising method for reducing deep neural networks’ complexity and power consumption significantly. Binarization techniques, however, suffer from ineligible performance degradation compared to their full-precision counterparts. Prior work mainly focused on strategies for sign function approximation during the forward and backward phases to reduce the quantization error during the binarization process. In this work, we propose a bimodal-distributed binarization method (BD-BNN). The newly proposed technique aims to impose a bimodal distribution of t

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Cho, Jaechan, Yongchul Jung, Seongjoo Lee, and Yunho Jung. "Reconfigurable Binary Neural Network Accelerator with Adaptive Parallelism Scheme." Electronics 10, no. 3 (2021): 230. http://dx.doi.org/10.3390/electronics10030230.

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Binary neural networks (BNNs) have attracted significant interest for the implementation of deep neural networks (DNNs) on resource-constrained edge devices, and various BNN accelerator architectures have been proposed to achieve higher efficiency. BNN accelerators can be divided into two categories: streaming and layer accelerators. Although streaming accelerators designed for a specific BNN network topology provide high throughput, they are infeasible for various sensor applications in edge AI because of their complexity and inflexibility. In contrast, layer accelerators with reasonable reso

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Sunny, Febin P., Asif Mirza, Mahdi Nikdast, and Sudeep Pasricha. "ROBIN: A Robust Optical Binary Neural Network Accelerator." ACM Transactions on Embedded Computing Systems 20, no. 5s (2021): 1–24. http://dx.doi.org/10.1145/3476988.

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Domain specific neural network accelerators have garnered attention because of their improved energy efficiency and inference performance compared to CPUs and GPUs. Such accelerators are thus well suited for resource-constrained embedded systems. However, mapping sophisticated neural network models on these accelerators still entails significant energy and memory consumption, along with high inference time overhead. Binarized neural networks (BNNs), which utilize single-bit weights, represent an efficient way to implement and deploy neural network models on accelerators. In this paper, we pres

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Simons, Taylor, and Dah-Jye Lee. "A Review of Binarized Neural Networks." Electronics 8, no. 6 (2019): 661. http://dx.doi.org/10.3390/electronics8060661.

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In this work, we review Binarized Neural Networks (BNNs). BNNs are deep neural networks that use binary values for activations and weights, instead of full precision values. With binary values, BNNs can execute computations using bitwise operations, which reduces execution time. Model sizes of BNNs are much smaller than their full precision counterparts. While the accuracy of a BNN model is generally less than full precision models, BNNs have been closing accuracy gap and are becoming more accurate on larger datasets like ImageNet. BNNs are also good candidates for deep learning implementation

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Wang, Peisong, Xiangyu He, Gang Li, Tianli Zhao, and Jian Cheng. "Sparsity-Inducing Binarized Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12192–99. http://dx.doi.org/10.1609/aaai.v34i07.6900.

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Binarization of feature representation is critical for Binarized Neural Networks (BNNs). Currently, sign function is the commonly used method for feature binarization. Although it works well on small datasets, the performance on ImageNet remains unsatisfied. Previous methods mainly focus on minimizing quantization error, improving the training strategies and decomposing each convolution layer into several binary convolution modules. However, whether sign is the only option for binarization has been largely overlooked. In this work, we propose the Sparsity-inducing Binarized Neural Network (Si-

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Liu, Chunlei, Peng Chen, Bohan Zhuang, Chunhua Shen, Baochang Zhang, and Wenrui Ding. "SA-BNN: State-Aware Binary Neural Network." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (2021): 2091–99. http://dx.doi.org/10.1609/aaai.v35i3.16306.

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Binary Neural Networks (BNNs) have received significant attention due to the memory and computation efficiency recently. However, the considerable accuracy gap between BNNs and their full-precision counterparts hinders BNNs to be deployed to resource-constrained platforms. One of the main reasons for the performance gap can be attributed to the frequent weight flip, which is caused by the misleading weight update in BNNs. To address this issue, we propose a state-aware binary neural network (SA-BNN) equipped with the well designed state-aware gradient. Our SA-BNN is inspired by the observation

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7

Zhao, Yiyang, Yongjia Wang, Ruibo Wang, Yuan Rong, and Xianyang Jiang. "A Highly Robust Binary Neural Network Inference Accelerator Based on Binary Memristors." Electronics 10, no. 21 (2021): 2600. http://dx.doi.org/10.3390/electronics10212600.

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Since memristor was found, it has shown great application potential in neuromorphic computing. Currently, most neural networks based on memristors deploy the special analog characteristics of memristor. However, owing to the limitation of manufacturing process, non-ideal characteristics such as non-linearity, asymmetry, and inconsistent device periodicity appear frequently and definitely, therefore, it is a challenge to employ memristor in a massive way. On the contrary, a binary neural network (BNN) requires its weights to be either +1 or −1, which can be mapped by digital memristors with hig

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8

Xiang, Maoyang, and Tee Hui Teo. "Implementation of Binarized Neural Networks in All-Programmable System-on-Chip Platforms." Electronics 11, no. 4 (2022): 663. http://dx.doi.org/10.3390/electronics11040663.

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The Binarized Neural Network (BNN) is a Convolutional Neural Network (CNN) consisting of binary weights and activation rather than real-value weights. Smaller models are used, allowing for inference effectively on mobile or embedded devices with limited power and computing capabilities. Nevertheless, binarization results in lower-entropy feature maps and gradient vanishing, which leads to a loss in accuracy compared to real-value networks. Previous research has addressed these issues with various approaches. However, those approaches significantly increase the algorithm’s time and space comple

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9

Zhang, Longlong, Xuebin Tang, Xiang Hu, Tong Zhou, and Yuanxi Peng. "FPGA-Based BNN Architecture in Time Domain with Low Storage and Power Consumption." Electronics 11, no. 9 (2022): 1421. http://dx.doi.org/10.3390/electronics11091421.

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With the increasing demand for convolutional neural networks (CNNs) in many edge computing scenarios and resource-limited settings, researchers have made efforts to apply lightweight neural networks on hardware platforms. While binarized neural networks (BNNs) perform excellently in such tasks, many implementations still face challenges such as an imbalance between accuracy and computational complexity, as well as the requirement for low power and storage consumption. This paper first proposes a novel binary convolution structure based on the time domain to reduce resource and power consumptio

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10

Kim, HyunJin, Mohammed Alnemari, and Nader Bagherzadeh. "A storage-efficient ensemble classification using filter sharing on binarized convolutional neural networks." PeerJ Computer Science 8 (March 29, 2022): e924. http://dx.doi.org/10.7717/peerj-cs.924.

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This paper proposes a storage-efficient ensemble classification to overcome the low inference accuracy of binary neural networks (BNNs). When external power is enough in a dynamic powered system, classification results can be enhanced by aggregating outputs of multiple BNN classifiers. However, memory requirements for storing multiple classifiers are a significant burden in the lightweight system. The proposed scheme shares the filters from a trained convolutional neural network (CNN) model to reduce storage requirements in the binarized CNNs instead of adopting the fully independent classifie

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11

Parmar, Vivek, Sandeep Kaur Kingra, Shubham Negi, and Manan Suri. "Analysis of VMM computation strategies to implement BNN applications on RRAM arrays." APL Machine Learning 1, no. 2 (2023): 026108. http://dx.doi.org/10.1063/5.0139583.

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The growing interest in edge-AI solutions and advances in the field of quantized neural networks have led to hardware efficient binary neural networks (BNNs). Extreme BNNs utilize only binary weights and activations, making them more memory efficient. Such networks can be realized using exclusive-NOR (XNOR) gates and popcount circuits. The analog in-memory realization of BNNs utilizing emerging non-volatile memory devices has been widely explored recently. However, most realizations typically use 2T-2R synapses, resulting in sub-optimal area utilization. In this study, we investigate alternate

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Trinh Quang Kien. "Improving the robustness of binarized neural network using the EFAT method." Journal of Military Science and Technology, CSCE5 (December 15, 2021): 14–23. http://dx.doi.org/10.54939/1859-1043.j.mst.csce5.2021.14-23.

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In recent years with the explosion of research in artificial intelligence, deep learning models based on convolutional neural networks (CNNs) are one of the promising architectures for practical applications thanks to their reasonably good achievable accuracy. However, CNNs characterized by convolutional layers often have a large number of parameters and computational workload, leading to large energy consumption for training and network inference. The binarized neural network (BNN) model has been recently proposed to overcome that drawback. The BNNs use binary representation for the inputs an

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13

Jiang, Xinrui, Nannan Wang, Jingwei Xin, Keyu Li, Xi Yang, and Xinbo Gao. "Training Binary Neural Network without Batch Normalization for Image Super-Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1700–1707. http://dx.doi.org/10.1609/aaai.v35i2.16263.

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Recently, binary neural network (BNN) based super-resolution (SR) methods have enjoyed initial success in the SR field. However, there is a noticeable performance gap between the binarized model and the full-precision one. Furthermore, the batch normalization (BN) in binary SR networks introduces floating-point calculations, which is unfriendly to low-precision hardwares. Therefore, there is still room for improvement in terms of model performance and efficiency. Focusing on this issue, in this paper, we first explore a novel binary training mechanism based on the feature distribution, allowin

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14

Yu, Jie, Woyu Zhang, Danian Dong, et al. "Long-Term Accuracy Enhancement of Binary Neural Networks Based on Optimized Three-Dimensional Memristor Array." Micromachines 13, no. 2 (2022): 308. http://dx.doi.org/10.3390/mi13020308.

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In embedded neuromorphic Internet of Things (IoT) systems, it is critical to improve the efficiency of neural network (NN) edge devices in inferring a pretrained NN. Meanwhile, in the paradigm of edge computing, device integration, data retention characteristics and power consumption are particularly important. In this paper, the self-selected device (SSD), which is the base cell for building the densest three-dimensional (3D) architecture, is used to store non-volatile weights in binary neural networks (BNN) for embedded NN applications. Considering that the prevailing issues in written data

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15

Zhu, Ganlin, Hongxiao Fei, Junkun Hong, Yueyi Luo, and Jun Long. "An Information-Reserved and Deviation-Controllable Binary Neural Network for Object Detection." Mathematics 11, no. 1 (2022): 62. http://dx.doi.org/10.3390/math11010062.

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Object detection is a fundamental task in computer vision, which is usually based on convolutional neural networks (CNNs). While it is difficult to be deployed in embedded devices due to the huge storage and computing consumptions, binary neural networks (BNNs) can execute object detection with limited resources. However, the extreme quantification in BNN causes diversity of feature representation loss, which eventually influences the object detection performance. In this paper, we propose a method balancing Information Retention and Deviation Control to achieve effective object detection, nam

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16

Simons, Taylor, and Dah-Jye Lee. "Efficient Binarized Convolutional Layers for Visual Inspection Applications on Resource-Limited FPGAs and ASICs." Electronics 10, no. 13 (2021): 1511. http://dx.doi.org/10.3390/electronics10131511.

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There has been a recent surge in publications related to binarized neural networks (BNNs), which use binary values to represent both the weights and activations in deep neural networks (DNNs). Due to the bitwise nature of BNNs, there have been many efforts to implement BNNs on ASICs and FPGAs. While BNNs are excellent candidates for these kinds of resource-limited systems, most implementations still require very large FPGAs or CPU-FPGA co-processing systems. Our work focuses on reducing the computational cost of BNNs even further, making them more efficient to implement on FPGAs. We target emb

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17

Xue, Ping, Yang Lu, Jingfei Chang, Xing Wei, and Zhen Wei. "Fast and Accurate Binary Neural Networks Based on Depth-Width Reshaping." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10684–92. http://dx.doi.org/10.1609/aaai.v37i9.26268.

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Network binarization (i.e., binary neural networks, BNNs) can efficiently compress deep neural networks and accelerate model inference but cause severe accuracy degradation. Existing BNNs are mainly implemented based on the commonly used full-precision network backbones, and then the accuracy is improved with various techniques. However, there is a question of whether the full-precision network backbone is well adapted to BNNs. We start from the factors of the performance degradation of BNNs and analyze the problems of directly using full-precision network backbones for BNNs: for a given compu

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18

Xi, Jiazhen, and Hiroyuki Yamauchi. "A Layer-Wise Ensemble Technique for Binary Neural Network." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 08 (2021): 2152011. http://dx.doi.org/10.1142/s021800142152011x.

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Binary neural networks (BNNs) have drawn much attention because of the most promising techniques to meet the desired memory footprint and inference speed requirements. However, they still suffer from the severe intrinsic instability of the error convergence, resulting in increase in prediction error and its standard deviation, which is mostly caused by the inherently poor representation with only two possible values of [Formula: see text]1 and [Formula: see text]1. In this work, we have proposed a cost-aware layer-wise ensemble method to address the above issue without incurring any excessive

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Gao, Jiabao, Qingliang Liu, and Jinmei Lai. "An Approach of Binary Neural Network Energy-Efficient Implementation." Electronics 10, no. 15 (2021): 1830. http://dx.doi.org/10.3390/electronics10151830.

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Binarized neural networks (BNNs), which have 1-bit weights and activations, are well suited for FPGA accelerators as their dominant computations are bitwise arithmetic, and the reduction in memory requirements means that all the network parameters can be stored in internal memory. However, the energy efficiency of these accelerators is still restricted by the abundant redundancies in BNNs. This hinders their deployment for applications in smart sensors and tiny devices because these scenarios have tight constraints with respect to energy consumption. To overcome this problem, we propose an app

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Chang, Liang, Xin Ma, Zhaohao Wang, Youguang Zhang, Yuan Xie, and Weisheng Zhao. "PXNOR-BNN: In/With Spin-Orbit Torque MRAM Preset-XNOR Operation-Based Binary Neural Networks." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 27, no. 11 (2019): 2668–79. http://dx.doi.org/10.1109/tvlsi.2019.2926984.

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Li, Yanfei, Tong Geng, Ang Li, and Huimin Yu. "BCNN: Binary complex neural network." Microprocessors and Microsystems 87 (November 2021): 104359. http://dx.doi.org/10.1016/j.micpro.2021.104359.

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Xu, Sheng, Chang Liu, Baochang Zhang, Jinhu Lü, Guodong Guo, and David Doermann. "BiRe-ID: Binary Neural Network for Efficient Person Re-ID." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 1s (2022): 1–22. http://dx.doi.org/10.1145/3473340.

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Person re-identification (Re-ID) has been promoted by the significant success of convolutional neural networks (CNNs). However, the application of such CNN-based Re-ID methods depends on the tremendous consumption of computation and memory resources, which affects its development on resource-limited devices such as next generation AI chips. As a result, CNN binarization has attracted increasing attention, which leads to binary neural networks (BNNs). In this article, we propose a new BNN-based framework for efficient person Re-ID (BiRe-ID). In this work, we discover that the significant perfor

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Zou, Wanbing, Song Cheng, Luyuan Wang, et al. "Increasing Information Entropy of Both Weights and Activations for the Binary Neural Networks." Electronics 10, no. 16 (2021): 1943. http://dx.doi.org/10.3390/electronics10161943.

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In terms of memory footprint requirement and computing speed, the binary neural networks (BNNs) have great advantages in power-aware deployment applications, such as AIoT edge terminals, wearable and portable devices, etc. However, the networks’ binarization process inevitably brings considerable information losses, and further leads to accuracy deterioration. To tackle these problems, we initiate analyzing from a perspective of the information theory, and manage to improve the networks information capacity. Based on the analyses, our work has two primary contributions: the first is a newly pr

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de Sousa, André L., Mário P. Véstias, and Horácio C. Neto. "Multi-Model Inference Accelerator for Binary Convolutional Neural Networks." Electronics 11, no. 23 (2022): 3966. http://dx.doi.org/10.3390/electronics11233966.

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Binary convolutional neural networks (BCNN) have shown good accuracy for small to medium neural network models. Their extreme quantization of weights and activations reduces off-chip data transfer and greatly reduces the computational complexity of convolutions. Further reduction in the complexity of a BCNN model for fast execution can be achieved with model size reduction at the cost of network accuracy. In this paper, a multi-model inference technique is proposed to reduce the execution time of the binarized inference process without accuracy reduction. The technique considers a cascade of n

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25

Peng, Hanyu, and Shifeng Chen. "BDNN: Binary convolution neural networks for fast object detection." Pattern Recognition Letters 125 (July 2019): 91–97. http://dx.doi.org/10.1016/j.patrec.2019.03.026.

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PENG, YUN, and MIAO JIN. "A NEURAL NETWORK APPROACH TO APPROXIMATING MAP IN BELIEF NETWORKS." International Journal of Neural Systems 12, no. 03n04 (2002): 271–90. http://dx.doi.org/10.1142/s0129065702001175.

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Bayesian belief networks (BBN) are a widely studied graphical model for representing uncertainty and probabilistic interdependence among variables. One of the factors that restricts the model's wide acceptance in practical applications is that the general inference with BBN is NP-hard. This is also true for the maximum a posteriori probability (MAP) problem, which is to find the most probable joint value assignment to all uninstantiated variables, given instantiation of some variables in a BBN . To circumvent the difficulty caused by MAP's computational complexity, we suggest in this paper a n

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Coluccio, Andrea, Marco Vacca, and Giovanna Turvani. "Logic-in-Memory Computation: Is It Worth It? A Binary Neural Network Case Study." Journal of Low Power Electronics and Applications 10, no. 1 (2020): 7. http://dx.doi.org/10.3390/jlpea10010007.

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Recently, the Logic-in-Memory (LiM) concept has been widely studied in the literature. This paradigm represents one of the most efficient ways to solve the limitations of a Von Neumann’s architecture: by placing simple logic circuits inside or near a memory element, it is possible to obtain a local computation without the need to fetch data from the main memory. Although this concept introduces a lot of advantages from a theoretical point of view, its implementation could introduce an increasing complexity overhead of the memory itself, leading to a more sophisticated design flow. As a case st

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Choi, Jeong Hwan, Young-Ho Gong, and Sung Woo Chung. "A System-Level Exploration of Binary Neural Network Accelerators with Monolithic 3D Based Compute-in-Memory SRAM." Electronics 10, no. 5 (2021): 623. http://dx.doi.org/10.3390/electronics10050623.

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Binary neural networks (BNNs) are adequate for energy-constrained embedded systems thanks to binarized parameters. Several researchers have proposed the compute-in-memory (CiM) SRAMs for XNOR-and-accumulation computations (XACs) in BNNs by adding additional transistors to the conventional 6T SRAM, which reduce the latency and energy of the data movements. However, due to the additional transistors, the CiM SRAMs suffer from larger area and longer wires than the conventional 6T SRAMs. Meanwhile, monolithic 3D (M3D) integration enables fine-grained 3D integration, reducing the 2D wire length in

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Gundersen, Kristian, Guttorm Alendal, Anna Oleynik, and Nello Blaser. "Binary Time Series Classification with Bayesian Convolutional Neural Networks When Monitoring for Marine Gas Discharges." Algorithms 13, no. 6 (2020): 145. http://dx.doi.org/10.3390/a13060145.

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The world’s oceans are under stress from climate change, acidification and other human activities, and the UN has declared 2021–2030 as the decade for marine science. To monitor the marine waters, with the purpose of detecting discharges of tracers from unknown locations, large areas will need to be covered with limited resources. To increase the detectability of marine gas seepage we propose a deep probabilistic learning algorithm, a Bayesian Convolutional Neural Network (BCNN), to classify time series of measurements. The BCNN will classify time series to belong to a leak/no-leak situation,

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Babu, Bileesh Plakkal, and Swathi Jamjala Narayanan. "One-vs-All Convolutional Neural Networks for Synthetic Aperture Radar Target Recognition." Cybernetics and Information Technologies 22, no. 3 (2022): 179–97. http://dx.doi.org/10.2478/cait-2022-0035.

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Abstract Convolutional Neural Networks (CNN) have been widely utilized for Automatic Target Recognition (ATR) in Synthetic Aperture Radar (SAR) images. However, a large number of parameters and a huge training data requirements limit CNN’s use in SAR ATR. While previous works have primarily focused on model compression and structural modification of CNN, this paper employs the One-Vs-All (OVA) technique on CNN to address these issues. OVA-CNN comprises several Binary classifying CNNs (BCNNs) that act as an expert in correctly recognizing a single target. The BCNN that predicts the highest prob

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Lee, Su-Jung, Gil-Ho Kwak, and Tae-Hwan Kim. "TORRES: A Resource-Efficient Inference Processor for Binary Convolutional Neural Networks Based on Locality-Aware Operation Skipping." Electronics 11, no. 21 (2022): 3534. http://dx.doi.org/10.3390/electronics11213534.

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A binary convolutional neural network (BCNN) is a neural network promising to realize analysis of visual imagery in low-cost resource-limited devices. This study presents an efficient inference processor for BCNNs, named TORRES. TORRES performs inference efficiently, skipping operations based on the spatial locality inherent in feature maps. The training process is regularized with the objective of skipping more operations. The microarchitecture is designed to skip operations and generate addresses efficiently with low resource usage. A prototype inference system based on TORRES has been imple

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Siddiqui, Shama, Rory Nesbitt, Muhammad Zeeshan Shakir, et al. "Artificial Neural Network (ANN) Enabled Internet of Things (IoT) Architecture for Music Therapy." Electronics 9, no. 12 (2020): 2019. http://dx.doi.org/10.3390/electronics9122019.

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Alternative medicine techniques such as music therapy have been a recent interest of medical practitioners and researchers. Significant clinical evidence suggests that music has a positive influence over pain, stress and anxiety for the patients of cancer, pre and post surgery, insomnia, child birth, end of life care, etc. Similarly, the technologies of Internet of Things (IoT), Body Area Networks (BAN) and Artificial Neural Networks (ANN) have been playing a vital role to improve the health and safety of the population through offering continuous remote monitoring facilities and immediate med

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Sarker, Ronobir, Amandeep Kaur, and D. Singh. "Noise Estimation Using Back Propagation Neural Networks." ECS Transactions 107, no. 1 (2022): 18761–68. http://dx.doi.org/10.1149/10701.18761ecst.

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In this paper, a new Backpropagation Neural Network-based noise estimation method is proposed to estimate Rician noise from MRI images. To train BNN features of MRI images such as contrast, homogeneity, dissimilarity, asm, energy, entropy, mean x, mean y, mean glcm, var x, var y, var glcm, correlation, skew x, skew y, skew, kurtosis x, kurtosis y, kurtosis, etc. are used. For training BNN, 450 images are used which are downloaded from BrainWeb.

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Wagner, Philipp, Xinyang Wu, and Marco F. Huber. "Kalman Bayesian Neural Networks for Closed-Form Online Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10069–77. http://dx.doi.org/10.1609/aaai.v37i8.26200.

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Compared to point estimates calculated by standard neural networks, Bayesian neural networks (BNN) provide probability distributions over the output predictions and model parameters, i.e., the weights. Training the weight distribution of a BNN, however, is more involved due to the intractability of the underlying Bayesian inference problem and thus, requires efficient approximations. In this paper, we propose a novel approach for BNN learning via closed-form Bayesian inference. For this purpose, the calculation of the predictive distribution of the output and the update of the weight distribut

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Park, Namuk, Taekyu Lee, and Songkuk Kim. "Vector Quantized Bayesian Neural Network Inference for Data Streams." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 9322–30. http://dx.doi.org/10.1609/aaai.v35i10.17124.

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Bayesian neural networks (BNN) can estimate the uncertainty in predictions, as opposed to non-Bayesian neural networks (NNs). However, BNNs have been far less widely used than non-Bayesian NNs in practice since they need iterative NN executions to predict a result for one data, and it gives rise to prohibitive computational cost. This computational burden is a critical problem when processing data streams with low-latency. To address this problem, we propose a novel model VQ-BNN, which approximates BNN inference for data streams. In order to reduce the computational burden, VQ-BNN inference pr

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McCormack, Michael D., David E. Zaucha, and Dennis W. Dushek. "First‐break refraction event picking and seismic data trace editing using neural networks." GEOPHYSICS 58, no. 1 (1993): 67–78. http://dx.doi.org/10.1190/1.1443352.

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Interactive seismic processing systems for editing noisy seismic traces and picking first‐break refraction events have been developed using a neural network learning algorithm. We employ a backpropagation neural network (BNN) paradigm modified to improve the convergence rate of the BNN. The BNN is interactively “trained” to edit seismic data or pick first breaks by a human processor who judiciously selects and presents to the network examples of trace edits or refraction picks. The network then iteratively adjusts a set of internal weights until it can accurately duplicate the examples provide

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Świetlicka, Aleksandra, Karol Gugała, Marta Kolasa, Jolanta Pauk, Andrzej Rybarczyk, and Rafał Długosz. "A New Model of the Neuron for Biological Spiking Neural Network Suitable for Parallel Data Processing Realized in Hardware." Solid State Phenomena 199 (March 2013): 217–22. http://dx.doi.org/10.4028/www.scientific.net/ssp.199.217.

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The paper presents a modification of the structure of a biological neural network (BNN) based on spiking neuron models. The proposed modification allows to influence the level of the stimulus response of particular neurons in the BNN. We consider an extended, three-dimensional Hodgkin-Huxley model of the neural cell. A typical BNN composed of such neural cells have been expanded by addition of resistors in each branch point. The resistors can be treated as the weights in such BNN. We demonstrate that adding these elements to the BNN significantly affects the waveform of the potential on the me

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Liang, Jiuzhen, Wei Song, and Mei Wang. "Stock Price Prediction Based on Procedural Neural Networks." Advances in Artificial Neural Systems 2011 (June 15, 2011): 1–11. http://dx.doi.org/10.1155/2011/814769.

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We present a spatiotemporal model, namely, procedural neural networks for stock price prediction. Compared with some successful traditional models on simulating stock market, such as BNN (backpropagation neural networks, HMM (hidden Markov model) and SVM (support vector machine)), the procedural neural network model processes both spacial and temporal information synchronously without slide time window, which is typically used in the well-known recurrent neural networks. Two different structures of procedural neural networks are constructed for modeling multidimensional time series problems. L

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Nguyen, Andre T., Fred Lu, Gary Lopez Munoz, Edward Raff, Charles Nicholas, and James Holt. "Out of Distribution Data Detection Using Dropout Bayesian Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7877–85. http://dx.doi.org/10.1609/aaai.v36i7.20757.

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We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data. We first show how previous attempts to leverage the randomized embeddings induced by the intermediate layers of a dropout BNN can fail due to the distance metric used. We introduce an alternative approach to measuring embedding uncertainty, and demonstrate how incorporating embedding uncertainty improves OOD data identification across three tasks: image classification, language classification, and malware detection.

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Jiang, Chuan Jin. "The Application of Bayesian Neural Network in Rainfall Forecasting." Key Engineering Materials 439-440 (June 2010): 1300–1305. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1300.

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The process of Rainfall Forecasting very complex and highly nonlinear and exhibits both temporal and spatial variability’s, In this article, a Rainfall Forecasting model using the Bayesian neural networks (BNN) is proposed for Rainfall Forecasting. The study uses the data from a coastal forest catchment. This article studies the accuracy of the short-term rainfall forecast obtained by BNN time-series analysis techniques and using antecedent rainfall depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of BNN Rainfall Forec

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Bae, Seongwoo, Haechan Kim, Seongjoo Lee, and Yunho Jung. "FPGA Implementation of Keyword Spotting System Using Depthwise Separable Binarized and Ternarized Neural Networks." Sensors 23, no. 12 (2023): 5701. http://dx.doi.org/10.3390/s23125701.

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Keyword spotting (KWS) systems are used for human–machine communications in various applications. In many cases, KWS involves a combination of wake-up-word (WUW) recognition for device activation and voice command classification tasks. These tasks present a challenge for embedded systems due to the complexity of deep learning algorithms and the need for optimized networks for each application. In this paper, we propose a depthwise separable binarized/ternarized neural network (DS-BTNN) hardware accelerator capable of performing both WUW recognition and command classification on a single device

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Yu, Haofan, Alyandra Hami Seno, Zahra Sharif Khodaei, and M. H. Ferri Aliabadi. "Structural Health Monitoring Impact Classification Method Based on Bayesian Neural Network." Polymers 14, no. 19 (2022): 3947. http://dx.doi.org/10.3390/polym14193947.

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This paper proposes a novel method for multi-class classification and uncertainty quantification of impact events on a flat composite plate with a structural health monitoring (SHM) system by using a Bayesian neural network (BNN). Most of the existing research in passive sensing has focused on deterministic approaches for impact detection and characterization. However, there are variability in impact location, angle and energy in real operational conditions which results in uncertainty in the diagnosis. Therefore, this paper proposes a reliability-based impact characterization method based on

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Herdiansah, Arief, Rohmat Indra Borman, Desi Nurnaningsih, Alfry Aristo J. Sinlae, and Rosyid Ridlo Al Hakim. "Klasifikasi Citra Daun Herbal Dengan Menggunakan Backpropagation Neural Networks Berdasarkan Ekstraksi Ciri Bentuk." JURIKOM (Jurnal Riset Komputer) 9, no. 2 (2022): 388. http://dx.doi.org/10.30865/jurikom.v9i2.4066.

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Since ancient times until now herbal plants have been used for treatment and have been applied in the world of health to this day. All parts of the plant can be used as medicine, one of which is the leaves. However, there are still many people who are not familiar with the medicinal leaves. This is because the leaves at first glance look almost the same, making it difficult to tell them apart. Actually, if you look closely, the leaves have characteristics that can be distinguished from one leaf to another. The purpose of this study is to classify images of herbal leaf species using the Backpro

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Guan, Qing-yang, and Wu Shuang. "Signal Detection in Satellite-Ground IoT Link Based on Blind Neural Network." Wireless Communications and Mobile Computing 2021 (May 6, 2021): 1–10. http://dx.doi.org/10.1155/2021/5547989.

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At present, there are many problems in satellite-ground IoT link signal detection. Due to the complex characteristics of the satellite-ground IoT link, including Doppler and multipath effect, especially in scenarios related to military fields, it is difficult to use traditional method and traditional cooperative communication methods for link signal detection. Therefore, this paper proposes an efficient detection of satellite-ground IoT link based on the blind neural network (BNN). The BNN includes two network structures, the data feature network and the error update network. Through multiple

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Chauhan, Krishan Kumar, Garima Joshi, Manjeet Kaur, and Renu Vig. "Semiconductor wafer defect classification using convolution neural network: a binary case." IOP Conference Series: Materials Science and Engineering 1225, no. 1 (2022): 012060. http://dx.doi.org/10.1088/1757-899x/1225/1/012060.

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Abstract With the multitude of steps used in semiconductor industry, automation is being practiced extensively in its manufacturing processes to guarantee quality of manufactured chips and improvement in production. At front end of line, wafer are probed and defective chips are segregated. From this data wafer bin map are generated, these show defects on the surface of wafers. If analysis of wafer bin map is done manually, this may result in incorrect categorization of defects due to human error and lack of judgement. Thus, the rationale behind this research study is to determine the scope of

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Zanotti, Tommaso, Francesco Maria Puglisi, and Paolo Pavan. "Energy-Efficient Non-Von Neumann Computing Architecture Supporting Multiple Computing Paradigms for Logic and Binarized Neural Networks." Journal of Low Power Electronics and Applications 11, no. 3 (2021): 29. http://dx.doi.org/10.3390/jlpea11030029.

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Different in-memory computing paradigms enabled by emerging non-volatile memory technologies are promising solutions for the development of ultra-low-power hardware for edge computing. Among these, SIMPLY, a smart logic-in-memory architecture, provides high reconfigurability and enables the in-memory computation of both logic operations and binarized neural networks (BNNs) inference. However, operation-specific hardware accelerators can result in better performance for a particular task, such as the analog computation of the multiply and accumulate operation for BNN inference, but lack reconfi

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JUNIOR, GERALDO BRAZ, LEONARDO DE OLIVEIRA MARTINS, ARISTÓFANES CORREA SILVA, and ANSELMO CARDOSO PAIVA. "COMPARISON OF SUPPORT VECTOR MACHINES AND BAYESIAN NEURAL NETWORKS PERFORMANCE FOR BREAST TISSUES USING GEOSTATISTICAL FUNCTIONS IN MAMMOGRAPHIC IMAGES." International Journal of Computational Intelligence and Applications 09, no. 04 (2010): 271–88. http://dx.doi.org/10.1142/s1469026810002914.

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Female breast cancer is a major cause of deaths in occidental countries. Computer-aided Detection (CAD) systems can aid radiologists to increase diagnostic accuracy. In this work, we present a comparison between two classifiers applied to the separation of normal and abnormal breast tissues from mammograms. The purpose of the comparison is to select the best prediction technique to be part of a CAD system. Each region of interest is classified through a Support Vector Machine (SVM) and a Bayesian Neural Network (BNN) as normal or abnormal region. SVM is a machine-learning method, based on the

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Biswal, Manas Ranjan, Tahesin Samira Delwar, Abrar Siddique, Prangyadarsini Behera, Yeji Choi, and Jee-Youl Ryu. "Pattern Classification Using Quantized Neural Networks for FPGA-Based Low-Power IoT Devices." Sensors 22, no. 22 (2022): 8694. http://dx.doi.org/10.3390/s22228694.

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With the recent growth of the Internet of Things (IoT) and the demand for faster computation, quantized neural networks (QNNs) or QNN-enabled IoT can offer better performance than conventional convolution neural networks (CNNs). With the aim of reducing memory access costs and increasing the computation efficiency, QNN-enabled devices are expected to transform numerous industrial applications with lower processing latency and power consumption. Another form of QNN is the binarized neural network (BNN), which has 2 bits of quantized levels. In this paper, CNN-, QNN-, and BNN-based pattern recog

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Vidyasagar, M. "Are analog neural networks better than binary neural networks?" Circuits, Systems, and Signal Processing 17, no. 2 (1998): 243–70. http://dx.doi.org/10.1007/bf01202855.

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Zeng, Xia, Zhengfeng Yang, Li Zhang, Xiaochao Tang, Zhenbing Zeng, and Zhiming Liu. "Safety Verification of Nonlinear Systems with Bayesian Neural Network Controllers." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 15278–86. http://dx.doi.org/10.1609/aaai.v37i12.26782.

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Bayesian neural networks (BNNs) retain NN structures with a probability distribution placed over their weights. With the introduced uncertainties and redundancies, BNNs are proper choices of robust controllers for safety-critical control systems. This paper considers the problem of verifying the safety of nonlinear closed-loop systems with BNN controllers over unbounded-time horizon. In essence, we compute a safe weight set such that as long as the BNN controller is always applied with weights sampled from the safe weight set, the controlled system is guaranteed to be safe. We propose a novel

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