使用浮点实数实现标准深度学习算法。这呈现了在可能没有专用浮点单元（FPU）的低端设备上实现它们的障碍。因此，Tinyml的研究人员认为可以使用Integer操作在低端设备上培训和运行深神经网络（DNN）的机器学习算法。本文在纯C ++中提出了Pocketnn，轻型和独立的概念概念框架，用于仅使用整数的DNN训练和推断。与其他方法不同，PocketNN直接在整数上运行，而无需任何显式量化算法或定制的定期点格式。这是通过口袋激活来实现的，这是一个用于整数DNN的激活函数系列，以及称为直接反馈对准（DFA）的新兴DNN训练算法。与标准BackPropagation（BP）不同，DFA独立列举每个图层，从而避免在使用仅具有整数操作的BP时是一个关键问题的整数溢出。我们使用Pocketnn在两个着名的数据集，Mnist和Fashion-Mnist上培训一些DNN。我们的实验表明，DNN与我们的PocketNN接受过的DNN培训，分别在MNIST和Fashion-Mnist数据集中获得了96.98％和87.7％的准确性。精度非常接近使用具有浮点实数操作的BP培训的等效DNN，使得精度降解分别为1.02％p和2.09％p。最后，我们的PocketNN为低端设备具有高兼容性和可移植性，因为它是开源的开源，并在纯C ++中实现，没有任何依赖项。

We introduce a method to train Quantized Neural Networks (QNNs) -neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At traintime the quantized weights and activations are used for computing the parameter gradients. During the forward pass, QNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations. As a result, power consumption is expected to be drastically reduced. We trained QNNs over the MNIST, CIFAR-10, SVHN and ImageNet datasets. The resulting QNNs achieve prediction accuracy comparable to their 32-bit counterparts. For example, our quantized version of AlexNet with 1-bit weights and 2-bit activations achieves 51% top-1 accuracy. Moreover, we quantize the parameter gradients to 6-bits as well which enables gradients computation using only bit-wise operation. Quantized recurrent neural networks were tested over the Penn Treebank dataset, and achieved comparable accuracy as their 32-bit counterparts using only 4-bits. Last but not least, we programmed a binary matrix multiplication GPU kernel with which it is possible to run our MNIST QNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The QNN code is available online.

The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a quantization scheme that allows inference to be carried out using integer-only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware. We also co-design a training procedure to preserve end-to-end model accuracy post quantization. As a result, the proposed quantization scheme improves the tradeoff between accuracy and on-device latency. The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated in ImageNet classification and COCO detection on popular CPUs.

由于神经网络变得更加强大，因此在现实世界中部署它们的愿望是一个上升的愿望;然而，神经网络的功率和准确性主要是由于它们的深度和复杂性，使得它们难以部署，尤其是在资源受限的设备中。最近出现了神经网络量化，以满足这种需求通过降低网络的精度来降低神经网络的大小和复杂性。具有较小和更简单的网络，可以在目标硬件的约束中运行神经网络。本文调查了在过去十年中开发的许多神经网络量化技术。基于该调查和神经网络量化技术的比较，我们提出了该地区的未来研究方向。

We study the problem of training and certifying adversarially robust quantized neural networks (QNNs). Quantization is a technique for making neural networks more efficient by running them using low-bit integer arithmetic and is therefore commonly adopted in industry. Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization, and certification of the quantized representation is necessary to guarantee robustness. In this work, we present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs. Inspired by advances in robust learning of non-quantized networks, our training algorithm computes the gradient of an abstract representation of the actual network. Unlike existing approaches, our method can handle the discrete semantics of QNNs. Based on QA-IBP, we also develop a complete verification procedure for verifying the adversarial robustness of QNNs, which is guaranteed to terminate and produce a correct answer. Compared to existing approaches, the key advantage of our verification procedure is that it runs entirely on GPU or other accelerator devices. We demonstrate experimentally that our approach significantly outperforms existing methods and establish the new state-of-the-art for training and certifying the robustness of QNNs.

Deep Neural Networks (DNN) have achieved state-of-the-art results in a wide range of tasks, with the best results obtained with large training sets and large models. In the past, GPUs enabled these breakthroughs because of their greater computational speed. In the future, faster computation at both training and test time is likely to be crucial for further progress and for consumer applications on low-power devices. As a result, there is much interest in research and development of dedicated hardware for Deep Learning (DL). Binary weights, i.e., weights which are constrained to only two possible values (e.g. -1 or 1), would bring great benefits to specialized DL hardware by replacing many multiply-accumulate operations by simple accumulations, as multipliers are the most space and powerhungry components of the digital implementation of neural networks. We introduce BinaryConnect, a method which consists in training a DNN with binary weights during the forward and backward propagations, while retaining precision of the stored weights in which gradients are accumulated. Like other dropout schemes, we show that BinaryConnect acts as regularizer and we obtain near state-of-the-art results with BinaryConnect on the permutation-invariant MNIST, CIFAR-10 and SVHN.

Recurrent neural networks (RNN) are the backbone of many text and speech applications. These architectures are typically made up of several computationally complex components such as; non-linear activation functions, normalization, bi-directional dependence and attention. In order to maintain good accuracy, these components are frequently run using full-precision floating-point computation, making them slow, inefficient and difficult to deploy on edge devices. In addition, the complex nature of these operations makes them challenging to quantize using standard quantization methods without a significant performance drop. We present a quantization-aware training method for obtaining a highly accurate integer-only recurrent neural network (iRNN). Our approach supports layer normalization, attention, and an adaptive piecewise linear (PWL) approximation of activation functions, to serve a wide range of state-of-the-art RNNs. The proposed method enables RNN-based language models to run on edge devices with $2\times$ improvement in runtime, and $4\times$ reduction in model size while maintaining similar accuracy as its full-precision counterpart.

Although weight and activation quantization is an effective approach for Deep Neural Network (DNN) compression and has a lot of potentials to increase inference speed leveraging bit-operations, there is still a noticeable gap in terms of prediction accuracy between the quantized model and the full-precision model. To address this gap, we propose to jointly train a quantized, bit-operation-compatible DNN and its associated quantizers, as opposed to using fixed, handcrafted quantization schemes such as uniform or logarithmic quantization. Our method for learning the quantizers applies to both network weights and activations with arbitrary-bit precision, and our quantizers are easy to train. The comprehensive experiments on CIFAR-10 and ImageNet datasets show that our method works consistently well for various network structures such as AlexNet, VGG-Net, GoogLeNet, ResNet, and DenseNet, surpassing previous quantization methods in terms of accuracy by an appreciable margin. Code available at https://github.com/Microsoft/LQ-Nets

受生物神经元的启发，激活功能在许多现实世界中常用的任何人工神经网络的学习过程中起着重要作用。文献中已经提出了各种激活功能，用于分类和回归任务。在这项工作中，我们调查了过去已经使用的激活功能以及当前的最新功能。特别是，我们介绍了多年来激活功能的各种发展以及这些激活功能的优势以及缺点或局限性。我们还讨论了经典（固定）激活功能，包括整流器单元和自适应激活功能。除了基于表征的激活函数的分类法外，还提出了基于应用的激活函数的分类法。为此，对MNIST，CIFAR-10和CIFAR-100等分类数据集进行了各种固定和自适应激活函数的系统比较。近年来，已经出现了一个具有物理信息的机器学习框架，以解决与科学计算有关的问题。为此，我们还讨论了在物理知识的机器学习框架中使用的激活功能的各种要求。此外，使用Tensorflow，Pytorch和Jax等各种机器学习库之间进行了不同的固定和自适应激活函数进行各种比较。

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.

胶囊网络（CAPSNET）是图像处理的新兴趋势。与卷积神经网络相反，CAPSNET不容易受到对象变形的影响，因为对象的相对空间信息在整个网络中保存。但是，它们的复杂性主要与胶囊结构和动态路由机制有关，这使得以其原始形式部署封闭式以由小型微控制器（MCU）供电的设备几乎是不合理的。在一个智力从云到边缘迅速转移的时代，这种高复杂性对在边缘的采用capsnets的采用构成了严重的挑战。为了解决此问题，我们提出了一个API，用于执行ARM Cortex-M和RISC-V MCUS中的量化capsnet。我们的软件内核扩展了ARM CMSIS-NN和RISC-V PULP-NN，以用8位整数作为操作数支持胶囊操作。随之而来的是，我们提出了一个框架，以执行CAPSNET的训练后量化。结果显示，记忆足迹的减少近75％，准确性损失范围从0.07％到0.18％。在吞吐量方面，我们的ARM Cortex-M API可以分别在仅119.94和90.60毫秒（MS）的中型胶囊和胶囊层执行（STM32H7555ZIT6U，Cortex-M7 @ 480 MHz）。对于GAP-8 SOC（RISC-V RV32IMCXPULP @ 170 MHz），延迟分别降至7.02和38.03 ms。

混合精确的深神经网络达到了硬件部署所需的能源效率和吞吐量，尤其是在资源有限的情况下，而无需牺牲准确性。但是，不容易找到保留精度的最佳每层钻头精度，尤其是在创建巨大搜索空间的大量模型，数据集和量化技术中。为了解决这一困难，最近出现了一系列文献，并且已经提出了一些实现有希望的准确性结果的框架。在本文中，我们首先总结了文献中通常使用的量化技术。然后，我们对混合精液框架进行了彻底的调查，该调查是根据其优化技术进行分类的，例如增强学习和量化技术，例如确定性舍入。此外，讨论了每个框架的优势和缺点，我们在其中呈现并列。我们最终为未来的混合精液框架提供了指南。

神经网络的外包计算允许用户访问艺术模型的状态，而无需投资专门的硬件和专业知识。问题是用户对潜在的隐私敏感数据失去控制。通过同性恋加密（HE）可以在加密数据上执行计算，而不会显示其内容。在这种知识的系统化中，我们深入了解与隐私保留的神经网络相结合的方法。我们将更改分类为神经网络模型和架构，使其在他和这些变化的影响方面提供影响。我们发现众多挑战是基于隐私保留的深度学习，例如通过加密方案构成的计算开销，可用性和限制。

代表低精度的深度神经网络（DNN）是一种有希望的方法来实现有效的加速和记忆力。以前的方法在低精度中培训DNN的方法通常在重量更新期间在高精度中保持重量的重量副本。由于低精度数字系统与学习算法之间的复杂相互作用，直接具有低精度重量的培训导致精度下降。为了解决这个问题，我们开发了一个共同设计的低精度训练框架，被称为LNS-MADAM，我们共同设计了对数号系统（LNS）和乘法权重算法（MADAM）。我们证明了LNS-MADAM在重量更新期间导致低量化误差，即使精度有限，也导致稳定的收敛。我们进一步提出了LNS-MADAM的硬件设计，可以解决实现LNS计算的有效数据路径的实际挑战。我们的实现有效地降低了LNS - 整数转换和部分总和累积所产生的能量开销。实验结果表明，LNS-MADAM为全精密对应物达到了可比的准确性，只有8位对流行的计算机视觉和自然语言任务。与全精密浮点实施相比，LNS-MADAM将能耗降低超过90。

While deep neural networks (DNNs) have demonstrated impressive performance in solving many challenging tasks, they are limited to resource-constrained devices owing to their demand for computation power and storage space. Quantization is one of the most promising techniques to address this issue by quantizing the weights and/or activation tensors of a DNN into lower bit-width fixed-point numbers. While quantization has been empirically shown to introduce minor accuracy loss, it lacks formal guarantees on that, especially when the resulting quantized neural networks (QNNs) are deployed in safety-critical applications. A majority of existing verification methods focus exclusively on individual neural networks, either DNNs or QNNs. While promising attempts have been made to verify the quantization error bound between DNNs and their quantized counterparts, they are not complete and more importantly do not support fully quantified neural networks, namely, only weights are quantized. To fill this gap, in this work, we propose a quantization error bound verification method (QEBVerif), where both weights and activation tensors are quantized. QEBVerif consists of two analyses: a differential reachability analysis (DRA) and a mixed-integer linear programming (MILP) based verification method. DRA performs difference analysis between the DNN and its quantized counterpart layer-by-layer to efficiently compute a tight quantization error interval. If it fails to prove the error bound, then we encode the verification problem into an equivalent MILP problem which can be solved by off-the-shelf solvers. Thus, QEBVerif is sound, complete, and arguably efficient. We implement QEBVerif in a tool and conduct extensive experiments, showing its effectiveness and efficiency.

With an ever-growing number of parameters defining increasingly complex networks, Deep Learning has led to several breakthroughs surpassing human performance. As a result, data movement for these millions of model parameters causes a growing imbalance known as the memory wall. Neuromorphic computing is an emerging paradigm that confronts this imbalance by performing computations directly in analog memories. On the software side, the sequential Backpropagation algorithm prevents efficient parallelization and thus fast convergence. A novel method, Direct Feedback Alignment, resolves inherent layer dependencies by directly passing the error from the output to each layer. At the intersection of hardware/software co-design, there is a demand for developing algorithms that are tolerable to hardware nonidealities. Therefore, this work explores the interrelationship of implementing bio-plausible learning in-situ on neuromorphic hardware, emphasizing energy, area, and latency constraints. Using the benchmarking framework DNN+NeuroSim, we investigate the impact of hardware nonidealities and quantization on algorithm performance, as well as how network topologies and algorithm-level design choices can scale latency, energy and area consumption of a chip. To the best of our knowledge, this work is the first to compare the impact of different learning algorithms on Compute-In-Memory-based hardware and vice versa. The best results achieved for accuracy remain Backpropagation-based, notably when facing hardware imperfections. Direct Feedback Alignment, on the other hand, allows for significant speedup due to parallelization, reducing training time by a factor approaching N for N-layered networks.

二进制神经网络（BNNS）已经证明了它们能够以可比精度（DNNS）的准确性来解决复杂任务的能力，同时还降低了计算能力和存储要求并提高处理速度。这些属性使它们成为开发和部署基于DNN的应用程序（IOT）设备的吸引人的替代方法。尽管最近有所改善，但它们的压缩因素可能会导致一些资源非常有限的设备可能导致不足。在这项工作中，我们提出了稀疏的二进制神经网络（SBNNS），这是一种新颖的模型和训练方案，它引入了BNN中的稀疏性和一种新的量化函数，以使网络的权重进行二进制。提出的SBNN能够达到高压因子，并减少了推理时的操作和参数数量。我们还提供工具来协助SBNN设计，同时尊重硬件资源约束。我们通过三个数据集的线性和卷积网络上的一组实验来研究我们方法对不同压缩因子的概括属性。我们的实验证实，SBNNS可以达到高压缩率，而不会损害概括，同时进一步降低了BNNS的操作，使SBNNS成为以廉价，低成本，限量资源的IOT设备和传感器部署DNN的可行选择。

Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a crucial role in determining the network's behavior during training. Our results show that deep networks can be trained using only 16-bit wide fixed-point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy. We also demonstrate an energy-efficient hardware accelerator that implements low-precision fixed-point arithmetic with stochastic rounding.

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.

我们日常生活中的深度学习是普遍存在的，包括自驾车，虚拟助理，社交网络服务，医疗服务，面部识别等，但是深度神经网络在训练和推理期间需要大量计算资源。该机器学习界主要集中在模型级优化（如深度学习模型的架构压缩），而系统社区则专注于实施级别优化。在其间，在算术界中提出了各种算术级优化技术。本文在模型，算术和实施级技术方面提供了关于资源有效的深度学习技术的调查，并确定了三种不同级别技术的资源有效的深度学习技术的研究差距。我们的调查基于我们的资源效率度量定义，阐明了较低级别技术的影响，并探讨了资源有效的深度学习研究的未来趋势。