Deep neural networks are state of the art methods for many learning tasks due to their ability to extract increasingly better features at each network layer. However, the improved performance of additional layers in a deep network comes at the cost of added latency and energy usage in feedforward inference. As networks continue to get deeper and larger, these costs become more prohibitive for real-time and energy-sensitive applications.To address this issue, we present BranchyNet, a novel deep network architecture that is augmented with additional side branch classifiers. The architecture allows prediction results for a large portion of test samples to exit the network early via these branches when samples can already be inferred with high confidence. BranchyNet exploits the observation that features learned at an early layer of a network may often be sufficient for the classification of many data points. For more difficult samples, which are expected less frequently, BranchyNet will use further or all network layers to provide the best likelihood of correct prediction. We study the BranchyNet architecture using several well-known networks (LeNet, AlexNet, ResNet) and datasets (MNIST, CIFAR10) and show that it can both improve accuracy and significantly reduce the inference time of the network.

Deep neural networks have long training and processing times. Early exits added to neural networks allow the network to make early predictions using intermediate activations in the network in time-sensitive applications. However, early exits increase the training time of the neural networks. We introduce QuickNets: a novel cascaded training algorithm for faster training of neural networks. QuickNets are trained in a layer-wise manner such that each successive layer is only trained on samples that could not be correctly classified by the previous layers. We demonstrate that QuickNets can dynamically distribute learning and have a reduced training cost and inference cost compared to standard Backpropagation. Additionally, we introduce commitment layers that significantly improve the early exits by identifying for over-confident predictions and demonstrate its success.

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches. These approaches aim for a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. The development of such approaches is among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology from a scientific environment with virtually unlimited computing resources into everyday's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. In particular, we focus on deep neural networks (DNNs), the predominant machine learning models of the past decade. We give a comprehensive overview of the vast literature that can be mainly split into three non-mutually exclusive categories: (i) quantized neural networks, (ii) network pruning, and (iii) structural efficiency. These techniques can be applied during training or as post-processing, and they are widely used to reduce the computational demands in terms of memory footprint, inference speed, and energy efficiency. We also briefly discuss different concepts of embedded hardware for DNNs and their compatibility with machine learning techniques as well as potential for energy and latency reduction. We substantiate our discussion with experiments on well-known benchmark datasets using compression techniques (quantization, pruning) for a set of resource-constrained embedded systems, such as CPUs, GPUs and FPGAs. The obtained results highlight the difficulty of finding good trade-offs between resource efficiency and predictive performance.

通过利用数据示例多样性，早期的exit网络最近成为一种突出的神经网络体系结构，以加速深度学习推断过程。但是，早期出口的中间分类器会引入其他计算开销，这对于资源约束的边缘人工智能（AI）不利。在本文中，我们提出了一种早期退出预测机制，以减少由早期EXIT网络支持的设备边缘共同指导系统中的设备计算开销。具体而言，我们设计了一个低复杂性模块，即出口预测指标，以指导一些明显的“硬”样品以绕过早期出口的计算。此外，考虑到不同的通信带宽，我们扩展了潜伏期感知的边缘推理的提前退出预测机制，该机制通过一些简单的回归模型适应了出口预测变量的预测阈值和早期EXEST网络的置信阈值。广泛的实验结果证明了退出预测因子在早期EXIT网络的准确性和设备计算开销之间取得更好的权衡。此外，与基线方法相比，在不同的带宽条件下，提出的延迟感知边缘推理的方法可以达到更高的推理精度。

Deep neural networks (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. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems.This article aims to provide a comprehensive tutorial and survey about the recent advances towards the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic co-designs, being proposed in academia and industry.The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the trade-offs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.

减少大深度学习模型的处理时间的问题是许多现实世界应用中的根本挑战。早期退出方法通过将附加内部分类器（IC）附加到神经网络的中间层来努力实现这一目标。 IC可以快速返回简单示例的预测，结果，降低整个模型的平均推理时间。但是，如果特定IC不决定早期回答，则其预测被丢弃，其计算有效地浪费。为了解决这个问题，我们引入零时间浪费（ZTW），这是一种新的方法，其中每个IC重用由其前辈返回的预测（1）在IC和（2）之间以相对于类似的方式组合先前输出之间的直接连接。我们对各个数据集和架构进行了广泛的实验，以证明ZTW实现了比最近提出的早期退出方法的其他更好的比例与推理时间权衡。

Learning deeper convolutional neural networks becomes a tendency in recent years. However, many empirical evidences suggest that performance improvement cannot be gained by simply stacking more layers. In this paper, we consider the issue from an information theoretical perspective, and propose a novel method Relay Backpropagation, that encourages the propagation of effective information through the network in training stage. By virtue of the method, we achieved the first place in ILSVRC 2015 Scene Classification Challenge. Extensive experiments on two challenging large scale datasets demonstrate the effectiveness of our method is not restricted to a specific dataset or network architecture. Our models will be available to the research community later.

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture. To address these limitations, we describe a method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections. Our method prunes redundant connections using a three-step method. First, we train the network to learn which connections are important. Next, we prune the unimportant connections. Finally, we retrain the network to fine tune the weights of the remaining connections. On the ImageNet dataset, our method reduced the number of parameters of AlexNet by a factor of 9×, from 61 million to 6.7 million, without incurring accuracy loss. Similar experiments with VGG-16 found that the total number of parameters can be reduced by 13×, from 138 million to 10.3 million, again with no loss of accuracy.

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture. To address these limitations, we describe a method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections. Our method prunes redundant connections using a three-step method. First, we train the network to learn which connections are important. Next, we prune the unimportant connections. Finally, we retrain the network to fine tune the weights of the remaining connections. On the ImageNet dataset, our method reduced the number of parameters of AlexNet by a factor of 9×, from 61 million to 6.7 million, without incurring accuracy loss. Similar experiments with VGG-16 found that the total number of parameters can be reduced by 13×, from 138 million to 10.3 million, again with no loss of accuracy.

培训广泛和深度神经网络（DNN）需要大量的存储资源，例如内存，因为在转发传播期间必须在存储器中保存中间激活数据，然后恢复以便向后传播。然而，由于硬件设计约束，诸如GPU之类的最先进的加速器（例如GPU）仅配备了非常有限的存储容量，这显着限制了在训练大规模DNN时的最大批量大小和性能加速。传统的记忆保存技术均受性能开销或受限互连带宽或特定互连技术的约束。在本文中，我们提出了一种新颖的记忆高效的CNN训练框架（称为Comet），利用错误界限的损耗压缩来显着降低训练的内存要求，以允许培训更大的模型或加速培训。不同于采用基于图像的有损压缩机（例如JPEG）的最先进的解决方案来压缩激活数据，我们的框架故意采用严格的错误控制机制来采用错误界限的损耗压缩。具体而言，我们对从改变的激活数据传播到梯度的压缩误差传播的理论分析，并经验探讨改变梯度对训练过程的影响。基于这些分析，我们优化了误报的损耗压缩，并提出了一种用于激活数据压缩的自适应误差控制方案。我们评估我们对最先进的解决方案的设计，其中包含五个广泛采用的CNN和Imagenet DataSet。实验表明，我们所提出的框架可以在基线训练中显着降低13.5倍，并分别在另一个最先进的基于压缩框架上的1.8倍，几乎没有准确性损失。

We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32× memory saving. In XNOR-Networks, both the filters and the input to convolutional layers are binary. XNOR-Networks approximate convolutions using primarily binary operations. This results in 58× faster convolutional operations (in terms of number of the high precision operations) and 32× memory savings. XNOR-Nets offer the possibility of running state-of-the-art networks on CPUs (rather than GPUs) in real-time. Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with a Binary-Weight-Network version of AlexNet is the same as the full-precision AlexNet. We compare our method with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these methods by large margins on ImageNet, more than 16% in top-1 accuracy. Our code is available at: http://allenai.org/plato/xnornet.

使用卷积神经网络（CNN）已经显着改善了几种图像处理任务，例如图像分类和对象检测。与Reset和Abseralnet一样，许多架构在创建时至少在一个数据集中实现了出色的结果。培训的一个关键因素涉及网络的正规化，这可以防止结构过度装备。这项工作分析了在过去几年中开发的几种正规化方法，显示了不同CNN模型的显着改进。该作品分为三个主要区域：第一个称为“数据增强”，其中所有技术都侧重于执行输入数据的更改。第二个，命名为“内部更改”，旨在描述修改神经网络或内核生成的特征映射的过程。最后一个称为“标签”，涉及转换给定输入的标签。这项工作提出了与关于正则化的其他可用调查相比的两个主要差异：（i）第一个涉及在稿件中收集的论文并非超过五年，并第二个区别是关于可重复性，即所有作品此处推荐在公共存储库中可用的代码，或者它们已直接在某些框架中实现，例如Tensorflow或Torch。

纯粹后的损害评估对于管理资源分配和执行有效响应至关重要。传统上，这种评估是通过野外侦察进行的，该侦察速度缓慢，危险且艰巨。取而代之的是，在本文中，我们进一步提出了通过卷积神经网络实施深度学习的想法，以便将建筑物的后卫星卫星图像分类为被洪水/损坏或未损坏的。该实验是在2017年哈维飓风之后使用的，该数据集采用了一个包含大休斯顿地区的纯种后卫星图像的数据集进行。本文实施了三个卷积神经网络模型体系结构，并配对了其他模型考虑，以实现高精度（超过99％），（超过99％），，超过99％），（超过99％）加强在殖民后灾难评估中有效使用机器学习。

海洋生态系统及其鱼类栖息地越来越重要，因为它们在提供有价值的食物来源和保护效果方面的重要作用。由于它们的偏僻且难以接近自然，因此通常使用水下摄像头对海洋环境和鱼类栖息地进行监测。这些相机产生了大量数字数据，这些数据无法通过当前的手动处理方法有效地分析，这些方法涉及人类观察者。 DL是一种尖端的AI技术，在分析视觉数据时表现出了前所未有的性能。尽管它应用于无数领域，但仍在探索其在水下鱼类栖息地监测中的使用。在本文中，我们提供了一个涵盖DL的关键概念的教程，该教程可帮助读者了解对DL的工作原理的高级理解。该教程还解释了一个逐步的程序，讲述了如何为诸如水下鱼类监测等挑战性应用开发DL算法。此外，我们还提供了针对鱼类栖息地监测的关键深度学习技术的全面调查，包括分类，计数，定位和细分。此外，我们对水下鱼类数据集进行了公开调查，并比较水下鱼类监测域中的各种DL技术。我们还讨论了鱼类栖息地加工深度学习的新兴领域的一些挑战和机遇。本文是为了作为希望掌握对DL的高级了解，通过遵循我们的分步教程而为其应用开发的海洋科学家的教程，并了解如何发展其研究，以促进他们的研究。努力。同时，它适用于希望调查基于DL的最先进方法的计算机科学家，以进行鱼类栖息地监测。

深度学习技术在各种任务中都表现出了出色的有效性，并且深度学习具有推进多种应用程序（包括在边缘计算中）的潜力，其中将深层模型部署在边缘设备上，以实现即时的数据处理和响应。一个关键的挑战是，虽然深层模型的应用通常会产生大量的内存和计算成本，但Edge设备通常只提供非常有限的存储和计算功能，这些功能可能会在各个设备之间差异很大。这些特征使得难以构建深度学习解决方案，以释放边缘设备的潜力，同时遵守其约束。应对这一挑战的一种有希望的方法是自动化有效的深度学习模型的设计，这些模型轻巧，仅需少量存储，并且仅产生低计算开销。该调查提供了针对边缘计算的深度学习模型设计自动化技术的全面覆盖。它提供了关键指标的概述和比较，这些指标通常用于量化模型在有效性，轻度和计算成本方面的水平。然后，该调查涵盖了深层设计自动化技术的三类最新技术：自动化神经体系结构搜索，自动化模型压缩以及联合自动化设计和压缩。最后，调查涵盖了未来研究的开放问题和方向。

未来的通信网络必须解决稀缺范围，以适应异质无线设备的广泛增长。无线信号识别对于频谱监视，频谱管理，安全通信等越来越重要。因此，对边缘的综合频谱意识有可能成为超越5G网络的新兴推动力。该领域的最新研究具有（i）仅关注单个任务 - 调制或信号（协议）分类 - 在许多情况下，该系统不足以对系统作用，（ii）考虑要么考虑雷达或通信波形（同质波形类别），（iii）在神经网络设计阶段没有解决边缘部署。在这项工作中，我们首次在无线通信域中，我们利用了基于深神经网络的多任务学习（MTL）框架的潜力，同时学习调制和信号分类任务，同时考虑异质无线信号，例如雷达和通信波形。在电磁频谱中。提出的MTL体系结构受益于两项任务之间的相互关系，以提高分类准确性以及使用轻型神经网络模型的学习效率。此外，我们还将对模型进行实验评估，并通过空中收集的样品进行了对模型压缩的第一手洞察力，以及在资源受限的边缘设备上部署的深度学习管道。我们在两个参考体系结构上展示了所提出的模型的显着计算，记忆和准确性提高。除了建模适用于资源约束的嵌入式无线电平台的轻型MTL模型外，我们还提供了一个全面的异质无线信号数据集，以供公众使用。

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the Im-ageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

由于最近在ML和IoT中的突破，部署机器学习（ML）在MilliWatt-Scale-Scale-Scale-Scale Edge设备（Tinyml）上正在越来越受欢迎。但是，Tinyml的功能受到严格的功率和计算约束的限制。 Tinyml中的大多数当代研究都集中在模型压缩技术上，例如模型修剪和量化，以适合低端设备上的ML模型。然而，由于积极的压缩迅速缩小了模型能力和准确性，因此通过现有技术获得的能源消耗和推理时间的改善是有限的。在保留其模型容量的同时，改善推理时间和/或降低功率的另一种方法是通过早期筛选网络。这些网络将中间分类器沿基线神经网络放置，如果中间分类器对其预测表现出足够的信心，则可以促进神经网络计算的早期退出。早期效果网络的先前工作集中在大型网络上，超出了通常用于Tinyml应用程序的功能。在本文中，我们讨论了将早期外观添加到最先进的小型CNN中的挑战，并设计了一种早期筛选架构T-RECX，以解决这些挑战。此外，我们开发了一种方法来减轻在最终退出中通过利用早期外观学到的高级代表性来减轻网络过度思考的影响。我们从MLPERF微小的基准套件中评估了三个CNN的T-RECX，用于图像分类，关键字发现和视觉唤醒单词检测任务。我们的结果表明，T-RECX提高了基线网络的准确性，并显着减少了微小CNN的平均推理时间。 T-RECX达到了32.58％的平均拖鞋降低，以换取所有评估模型的1％精度。此外，我们的技术提高了我们评估的三个模型中的两个基线网络的准确性

具有早期退出机制的最先进的神经网络通常需要大量的培训和微调，以通过低计算成本来实现良好的性能。我们提出了一种新颖的早期出口技术，基于样本的类手段，提前出口课程（E $^2 $ cm）。与大多数现有方案不同，E $^2 $ cm不需要基于梯度的内部分类器培训，并且不会通过任何方式修改基本网络。这使其对于低功率设备的神经网络培训特别有用，如无线边缘网络。我们评估了E $^2 $ cm的性能和间接费用，例如MobileNetV3，EdgisterNet，Resnet和数据集，例如CIFAR-100，Imagenet和KMNIST。我们的结果表明，鉴于固定的培训时间预算，与现有的早期退出机制相比，E $^2 $ cm的准确性更高。此外，如果培训时间预算没有限制，则可以将E $^2 $ cm与现有的早期退出计划相结合，以提高后者的性能，从而在计算成本和网络准确性之间取得更好的权衡。我们还表明，E $^2 $ cm可用于降低无监督学习任务中的计算成本。

在物联网（IoT）支持的网络边缘（IOT）上的人工智能（AI）的最新进展已通过启用低延期性和计算效率来实现多种应用程序（例如智能农业，智能医院和智能工厂）的优势情报。但是，部署最先进的卷积神经网络（CNN），例如VGG-16和在资源约束的边缘设备上的重新连接，由于其大量参数和浮点操作（Flops），因此实际上是不可行的。因此，将网络修剪作为一种模型压缩的概念正在引起注意在低功率设备上加速CNN。结构化或非结构化的最先进的修剪方法都不认为卷积层表现出的复杂性的不同基本性质，并遵循训练放回训练的管道，从而导致其他计算开销。在这项工作中，我们通过利用CNN的固有层层级复杂性来提出一种新颖和计算高效的修剪管道。与典型的方法不同，我们提出的复杂性驱动算法根据其对整体网络复杂性的贡献选择了特定层用于滤波器。我们遵循一个直接训练修剪模型并避免计算复杂排名和微调步骤的过程。此外，我们定义了修剪的三种模式，即参数感知（PA），拖网（FA）和内存感知（MA），以引入CNN的多功能压缩。我们的结果表明，我们的方法在准确性和加速方面的竞争性能。最后，我们提出了不同资源和准确性之间的权衡取舍，这对于开发人员在资源受限的物联网环境中做出正确的决策可能会有所帮助。