Download or read book Neural Networks with Model Compression written by Baochang Zhang and published by Springer. This book was released on 2024-01-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has achieved impressive results in image classification, computer vision and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floating-point operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, our book will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS due to its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge about machine learning and deep learning to better understand the methods described in this book.

Download or read book Model Compression for Efficient Machine Learning Inference written by Sunwoo Kim and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents model compression methods to facilitate the practicality of deep learning and machine learning frameworks for real-time applications. Starting from conventional compression techniques such as quantization to reduce bit-widths, we extend to developing novel and compact frameworks through a lossless compression approach. We begin with an extreme network quantization algorithm to compress a floating-point deep neural network using single bit representations. The training is done in two rounds to preserve the model performance, first in a weight compressed real-valued network and then in a bitwise version with the same topology. The pretrained weights of the first round are used to initialize the weights of the bitwise network, where we redefine the feedforward procedure with bitwise values and operations. Only the bitwise network is used for deployment for test time inference, which not only makes it easier to put on small devices but also expedites the inference speed with bitwise arithmetic operations. For this study, we aim at compressing a recurrent neural network architecture for single-channel source separation. Applying extreme quantization on this type of network poses additional challenges due to its complex recurrent relations as quantization noise can accumulate over multiple time frames. We address this by proposing a more delicate solution to incrementally binarize the model parameters in order to minimize the potential loss that can occur from a sudden introduction of quantization. As the proposed binarization technique turns only a few randomly chosen parameters into their binary versions, it gives the network training procedure a chance to gently adapt to the partly quantized version of the network. It eventually achieves the full binarization by incrementally increasing the amount of binarization over the iterations. Binarization can be extended to data compression to provide the same benefits of extreme compression rates and expedited inference speeds using supported algorithms and hardware. Similarly to binarizing model weights, we propose to compress the bitwidths of data down to binary form with emphasis on minimizing loss of information. To this end, we introduce locality sensitive hash functions (LSH) to reduce the storage overhead while preserving the semantic similarity between the high-dimensional data points in the Euclidean space and binary codes. However, given the random nature of LSH projection vectors, a large bitstring is required to form discriminative hash codes that can guarantee high precision. In this dissertation, we propose to learn the locality sensitive hash functions using boosting theory to efficiently encode the underlying structure of data into hash codes. Our adaptive boosting algorithm learns simple logistic regressors as the weak learners. The algorithm differs from AdaBoost in the sense that the projections are trained to minimize the distances between the self-similarity matrix of the hash codes and that of the original data points, rather than the misclassification rate. We evaluate our discriminative hash codes on a source separation problem framed as a similarity search task. Upon training our hash functions, their binary classification results transform each data point into a bit string, on which simple bitwise operations calculate Hamming distance to find the nearest neighbors from the hashed dictionary. Quantization and other model compression methods can achieve good compression rates, but they are applied as a post-training procedure that propagate noise and decrease generalization performance. Quantization-aware training helps to minimize the accuracy drop by simulating the low precision inference using the same floating point backpropagation, there is a limit to the amount of recovery from this fine-tuning procedure. Furthermore, quantized models demand dedicated hardware designs to support bit-level manipulation in memory and computation units to reap the benefits from model reduction. We address this worsened generalization and hardware compatibility issue of model compression methods by improving compact models to outperform larger model counterparts as a form of lossless compression. The first approach is personalization, in which small models are fine-tuned to their test-time specificity. Personalized compact models are trained in original floating-point values without structural modifications, and do not require any specialized hardware. We aim at use-cases for end-user devices in realistic settings where we often encounter only a few classes within a target domain that tend to reoccur in the specific environment. Hence, we postulate a small personalized model suffices to handle this focused subset of the original universal problem. Our goal in this test-time adaptation is to develop personalized speech enhancement model targeting edge-devices that can perform well for relevant users' voices and surrounding acoustics (e.g. a family-owned smart assistant device). One major challenge for personalization is a major data shortage issue due to recent privacy infringement and data leakage issues. Our goal in this test-time adaptation is to perform personalized speech enhancement without utilizing clean speech target of the test speaker using a knowledge distillation framework. We distill the denoising results from an overly large teacher model, and use them as the pseudo target to train the small student model. Experimental results show that the personalized models outperform larger non-personalized baseline models, demonstrating that personalization achieves model compression with no loss of denoising performance. Finally, we propose another lossless approach using evolutionary algorithms to optimize compact generative adversarial networks. We coordinate the adversarial characteristics with a coevolutionary strategy and evolve a population of models to achieve high fitness corresponding to generative performance and training stability. Our framework exposes individuals to not only various but also fit and stronger adversaries per generation to learn robust and compact models for efficient and faster inference. The experimental results demonstrate generative models trained using the proposed coevolutionary strategy can produce small models capable of outperforming larger counterparts trained under the regular adversarial framework.

Download or read book Neural Network Model Compression Via Tensor Train Decomposition with Applications to Image Classification and Segmentation written by 韓呈宥 and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Download or read book Neural Networks Model Compression written by Sara Elkerdawy and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural networks (DNN) have emerged as the state-of-the-art method in several research areas. DNN is yet to fully permeate resource-constrained computing platforms, such as mobile phones. Accurate DNN models being deeper and wider take considerable memory and time to execute on small devices posing challenges to many significant real-time applications, e.g., robotics and augmented reality applications. Considerations for memory and power consumption are as important for low-end devices as they are for cloud-based with multiple graphical processing units (GPUs). In cloud-based solutions, factors such as performance-per-watt, performance-per-dollar, and throughput are important. Recently, different techniques were proposed to tackle the computational and memory issues inherent in DNN. We focus on neural network model pruning and distillation for inference and training acceleration respectively. First, early work in model pruning often relied on performing sensitivity analysis before pruning to set the pruning ratio per layer. This process is computationally expensive and hinders scalability for deeper, larger, and more connectivity complex models. We propose to train a binary mask for each convolutional filter that acts as a learnable pruning gate. In training, we encourage smaller models by inducing sparsity by minimizing the l1-norm of the masks. The task and pruning loss are trained jointly to allow for end-to-end fine-tuning and pruning. Second, we present a layer pruning framework for hardware-friendly pruned models optimized for latency reduction. Our layer pruning framework is a twofold contribution. One, we present a one-shot accuracy approximation by imprinting for layer ranking. We rank layers based on the difference between their approximated accuracy and that of the previous layer. Second, we adopt statistical criteria from filter pruning literature for layer ranking and examined both iterative filter pruning and layer pruning training paradigms under similar importance criteria in terms of accuracy and latency reduction. Third, we propose a dynamic filter pruning inference method to tackle diminishing accuracy gain from adding more neurons. Motivated by the popular saying in neuroscience: "neurons that fire together wire together", we propose to equip each convolution layer with a binary mask predictor that selects a handful of filters to process in the next layer given the input feature maps. We pose the problem as a supervised binary classification problem. Each mask predictor module is trained to estimate the log-likelihood for each filter in the next layer to belong to the top-k activated filters. Finally, we propose a distillation pipeline to accelerate the training of vision transformers. We adopt 1) self-distillation loss, and 2) query efficient teacher-study distillation loss. In self-distillation training, early layers mimic the output of the final layer within the same model. This achieves 2.8x speedup in comparison to teacher-student distillation with matched accuracy in many cases. We also propose a simple yet effective query-efficient distillation in case a trained teacher is available to further boost the accuracy. We query the teacher model (CNN) only when the student (transformer) fails to predict the correct output. This simple criterion not only saves computational resources but also achieves higher accuracy than a full query teacher-student.

Download or read book 2021 International Joint Conference on Neural Networks IJCNN written by IEEE Staff and published by . This book was released on 2021-07-18 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: JCNN is the premier international conference on neural networks theory, analysis, and a wide range of applications IJCNN 2021 is a truly interdisciplinary event with a broad range of contributions on recent advances in neural networks, including neuroscience and cognitive science, computational intelligence and machine learning, hybrid techniques, nonlinear dynamics and chaos, various soft computing technologies, bioinformatics and biomedicine, and engineering applications

Download or read book Algorithms and Architectures for Parallel Processing written by Meikang Qiu and published by Springer Nature. This book was released on 2020-09-29 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set LNCS 12452, 12453, and 12454 constitutes the proceedings of the 20th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2020, in New York City, NY, USA, in October 2020. The total of 142 full papers and 5 short papers included in this proceedings volumes was carefully reviewed and selected from 495 submissions. ICA3PP is covering the many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental projects, and commercial components and systems. As applications of computing systems have permeated in every aspects of daily life, the power of computing system has become increasingly critical. This conference provides a forum for academics and practitioners from countries around the world to exchange ideas for improving the efficiency, performance, reliability, security and interoperability of computing systems and applications. ICA3PP 2020 focus on two broad areas of parallel and distributed computing, i.e. architectures, algorithms and networks, and systems and applications.

Download or read book Deep Learning Applications Volume 2 written by M. Arif Wani and published by Springer. This book was released on 2020-12-14 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Download or read book Mixed Low bit Quantization for Model Compression with Layer Importance and Gradient Estimations written by Hongyang Liu and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural networks (DNNs) have been widely used in the modern world in recent years. However, due to the substantial memory consumption and high computational power use of DNNs, deploying them on devices with limited resources is challenging. Model compression methods can provide us with a remedy here. Among those techniques, neural network quantization has achieved a high compression rate using a low bitwidth representation of weights and activations while maintaining the accuracy of the high-precision original network. However, mixed precision (per-layer bit-width precision) quantization requires careful tuning to maintain accuracy while achieving further compression and higher granularity than fixed precision quantization. In this thesis, we propose an accuracy-aware criterion to quantify the layer's importance rank. Our method applies imprinting per layer, which acts as a proxy module for accuracy estimation in an efficient way. We rank the layers based on the accuracy gain from previous modules and iteratively quantize those with less accuracy. Previous mixed-precision methods either rely on expensive search techniques such as reinforcement learning (RL) or end-to-end optimization with a lack of interpretation to the quantization configuration scheme. Our method is a one-shot, efficient, accuracy-aware information estimation and thus draws better interpretability to the selected bit-width configuration. We have also pointed out the problem of the Straight-Through Estimator (STE), which is commonly used for gradients estimation in the quantization field. We've discussed some ways to address the problem of using STE.

Download or read book Compensatory Genetic Fuzzy Neural Networks and Their Applications written by Yan-Qing Zhang and published by World Scientific. This book was released on 1998 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games and pattern recognition. This effective soft computing system is able to perform both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also proposes various novel soft computing techniques.

Download or read book Modern Deep Learning Design and Application Development written by Andre Ye and published by Apress. This book was released on 2021-11-28 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking. You’ll begin with a structured guide to using Keras, with helpful tips and best practices for making the most of the framework. Next, you’ll learn how to train models effectively with transfer learning and self-supervised pre-training. You will then learn how to use a variety of model compressions for practical usage. Lastly, you will learn how to design successful neural network architectures and creatively reframe difficult problems into solvable ones. You’ll learn not only to understand and apply methods successfully but to think critically about it. Modern Deep Learning Design and Methods is ideal for readers looking to utilize modern, flexible, and creative deep-learning design and methods. Get ready to design and implement innovative deep-learning solutions to today’s difficult problems. What You’ll Learn Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization. Compress deep learning models while maintaining performance. Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them. Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches. Who This Book Is For Data scientists with some familiarity with deep learning to deep learning engineers seeking structured inspiration and direction on their next project. Developers interested in harnessing modern deep learning methods to solve a variety of difficult problems.

Download or read book Intelligent Computing Theories and Application written by De-Shuang Huang and published by Springer Nature. This book was released on 2021-08-09 with total page 913 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNCS 12836 and LNCS 12837 constitutes - in conjunction with the volume LNAI 12838 - the refereed proceedings of the 17th International Conference on Intelligent Computing, ICIC 2021, held in Shenzhen, China in August 2021. The 192 full papers of the three proceedings volumes were carefully reviewed and selected from 458 submissions. The ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. The theme for this conference is “Advanced Intelligent Computing Methodologies and Applications.” The papers are organized in the following subsections: Evolutionary Computation and Learning, Image and signal Processing, Information Security, Neural Networks, Pattern Recognition Swarm Intelligence and Optimization, and Virtual Reality and Human-Computer Interaction.

Download or read book Artificial Neural Networks Models for Image Data Compression microform written by Hazem M. Abbas and published by National Library of Canada = Bibliothèque nationale du Canada. This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Co designing Model Compression Algorithms and Hardware Accelerators for Efficient Deep Learning written by Ritchie Zhao and published by . This book was released on 2020 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past decade, machine learning (ML) with deep neural networks (DNNs) has become extremely successful in a variety of application domains including computer vision, natural language processing, and game AI. DNNs are now a primary topic of academic research among computer scientists, and a key component of commercial technologies such as web search, recommendation systems, and self-driving vehicles. However, factors such as the growing complexity of DNN models, the diminished benefits of technology scaling, and the proliferation of resource-constrained edge devices are driving a demand for higher DNN performance and energy efficiency. Consequently, neural network training and inference have begun to shift from commodity general-purpose processors (e.g., CPUs and GPUs) to custom-built hardware accelerators (e.g., FPGAs and ASICs). In line with this trend, there has been extensive research on specialized algorithms and architectures for dedicated DNN processors. Furthermore, the rapid pace of innovation in DNN algorithm space is mismatched with the time-consuming process of hardware implementation. This has generated increased interest in novel design methodologies and tools which can reduce the human effort and turn-around time of hardware design. This thesis studies how low-precision quantization and structured matrices can improve the performance and energy efficiency of DNNs running on specialized accelerators. We co-design both the DNN compression algorithms and the accelerator architectures, enabling us to evaluate the impact of our ideas on real hardware. In the process, we examine the use of high-level synthesis tools in reducing the hardware design effort. This thesis represents a cross-domain research effort at efficient deep learning. First, we propose specialized architectures for accelerating binarized neural networks on FPGA. Second, we study novel high-level synthesis techniques to reduce the manual effort in FPGA accelerator design. Third, we show a fundamental link between group convolutions and circulant matrices, two previously disparate lines of research in DNN compression. Using this insight we propose HadaNet, an alternative to circulant compression which achieve identical accuracy with asymptotically fewer multiplications. Fourth, we present outlier channel splitting, a technique to improve DNN weight quantization by removing outliers from the weight distribution without arduous retraining. Finally, we show preliminary results on overwrite quantization, a technique which address outliers in DNN activation quantization using extremely lightweight architectural extensions to a spatial accelerator template.

Download or read book Low rank Compression of Neural Networks written by Yerlan Idelbayev and published by . This book was released on 2021 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks have gained widespread use in many machine learning tasks due to their state-of-the-art performance. However, the cost of this progress lies in the ever-increasing sizes and computational demands of the resulting models. As such, the neural network compression, the process of reducing the size, power consumption, or any other cost of interest of the model, has become an important practical step when deploying the trained models to perform inference tasks. In this dissertation, we explore a particular compression mechanism --- the low-rank decomposition --- and its extensions for the purposes of neural network compression. We study important aspects of the low-rank compression: how to select the decomposition ranks across the layers, how to choose best decomposition shapes for non-matrix weights among a number of options, and how to adapt the low-rank scheme to target the inference speed. Computationally, these are hard problems involving integer variables (ranks, decomposition shapes) and continuous variables (weights), as well as nonlinear loss and constraints.As we show over the course of this dissertation, all these problems admit suitable formulations that can be efficiently solved using the recently proposed \emph{learning-compression algorithm}. The algorithm relies on the alternation of two optimization steps: the step over the neural network parameters, the L step, and the step over the compression parameters, the C step. Once we formulate the compression problems, we show how the L and C steps are derived. Each step can be solved efficiently: the L step is solved by stochastic gradient descent, and the C step relies on singular value decomposition. We demonstrate the effectiveness of the proposed compression schemes and the corresponding algorithms on multiple networks and datasets. Finally, we discuss the resulting general neural network compression toolkit that encompasses all compression schemes presented in this dissertation and many others. The toolkit is designed to be flexible and extensible, and is released under the open-source license.

Download or read book Artificial Neural Networks and Machine Learning ICANN 2021 written by Igor Farkaš and published by Springer Nature. This book was released on 2021-09-10 with total page 703 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as model compression, multi-task and multi-label learning, neural network theory, normalization and regularization methods, person re-identification, recurrent neural networks, and reinforcement learning. *The conference was held online 2021 due to the COVID-19 pandemic.

Download or read book Neural Information Processing written by Tom Gedeon and published by Springer Nature. This book was released on 2019-12-05 with total page 802 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. For ICONIP 2019 a total of 345 papers was carefully reviewed and selected for publication out of 645 submissions. The 168 papers included in this volume set were organized in topical sections as follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred computing; human centred computing and medicine; human centred computing for emotion; hybrid models; image processing by neural techniques; learning from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models.