用于压缩神经网络的非均匀量化策略通常实现的性能比其对应于对应物，即统一的策略，因为其优越的代表性能力。然而，许多非均匀量化方法在实现不均匀量化的权重/激活时忽略了复杂的投影过程，这在硬件部署中引起了不可忽略的时间和空间开销。在这项研究中，我们提出了非均匀致均匀的量化（N2UQ），一种方法，其能够保持非均匀方法的强表示能力，同时硬件友好且有效地作为模型推理的均匀量化。我们通过学习灵活的等距输入阈值来实现这一目标，以更好地拟合潜在的分布，同时将这些实值输入量化为等距输出电平。要使用可学习的输入阈值训练量化网络，我们将广义直通估计器（G-STE）介绍，用于难以应答的后向衍生计算W.r.t.阈值参数。此外，我们考虑熵保持正则化，以进一步降低重量量化的信息损失。即使在这种不利约束的施加均匀量化的重量和激活的情况下，我们的N2UQ也经历了最先进的非均匀量化方法，在想象中达到了0.7〜1.8％，展示了N2UQ设计的贡献。代码将公开可用。

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

为了以计算有效的方式部署深层模型，经常使用模型量化方法。此外，由于新的硬件支持混合的位算术操作，最近对混合精度量化（MPQ）的研究开始通过搜索网络中不同层和模块的优化位低宽，从而完全利用表示的能力。但是，先前的研究主要是在使用强化学习，神经体系结构搜索等的昂贵方案中搜索MPQ策略，或者简单地利用部分先验知识来进行位于刻度分配，这可能是有偏见和优势的。在这项工作中，我们提出了一种新颖的随机量化量化（SDQ）方法，该方法可以在更灵活，更全球优化的空间中自动学习MPQ策略，并具有更平滑的梯度近似。特别是，可区分的位宽参数（DBP）被用作相邻位意选择之间随机量化的概率因素。在获取最佳MPQ策略之后，我们将进一步训练网络使用熵感知的bin正则化和知识蒸馏。我们广泛评估了不同硬件（GPU和FPGA）和数据集的多个网络的方法。 SDQ的表现优于所有最先进的混合或单个精度量化，甚至比较低的位置量化，甚至比各种重新网络和Mobilenet家族的全精度对应物更好，这表明了我们方法的有效性和优势。

Although considerable progress has been obtained in neural network quantization for efficient inference, existing methods are not scalable to heterogeneous devices as one dedicated model needs to be trained, transmitted, and stored for one specific hardware setting, incurring considerable costs in model training and maintenance. In this paper, we study a new vertical-layered representation of neural network weights for encapsulating all quantized models into a single one. With this representation, we can theoretically achieve any precision network for on-demand service while only needing to train and maintain one model. To this end, we propose a simple once quantization-aware training (QAT) scheme for obtaining high-performance vertical-layered models. Our design incorporates a cascade downsampling mechanism which allows us to obtain multiple quantized networks from one full precision source model by progressively mapping the higher precision weights to their adjacent lower precision counterparts. Then, with networks of different bit-widths from one source model, multi-objective optimization is employed to train the shared source model weights such that they can be updated simultaneously, considering the performance of all networks. By doing this, the shared weights will be optimized to balance the performance of different quantized models, thus making the weights transferable among different bit widths. Experiments show that the proposed vertical-layered representation and developed once QAT scheme are effective in embodying multiple quantized networks into a single one and allow one-time training, and it delivers comparable performance as that of quantized models tailored to any specific bit-width. Code will be available.

模型二进制化是一种压缩神经网络并加速其推理过程的有效方法。但是，1位模型和32位模型之间仍然存在显着的性能差距。实证研究表明，二进制会导致前进和向后传播中的信息损失。我们提出了一个新颖的分布敏感信息保留网络（DIR-NET），该网络通过改善内部传播和引入外部表示，将信息保留在前后传播中。 DIR-NET主要取决于三个技术贡献：（1）最大化二进制（IMB）的信息：最小化信息损失和通过重量平衡和标准化同时同时使用权重/激活的二进制误差； （2）分布敏感的两阶段估计器（DTE）：通过共同考虑更新能力和准确的梯度来通过分配敏感的软近似来保留梯度的信息； （3）代表性二进制 - 意识蒸馏（RBD）：通过提炼完整精确和二元化网络之间的表示来保留表示信息。 DIR-NET从统一信息的角度研究了BNN的前进过程和后退过程，从而提供了对网络二进制机制的新见解。我们的DIR-NET中的三种技术具有多功能性和有效性，可以在各种结构中应用以改善BNN。关于图像分类和客观检测任务的综合实验表明，我们的DIR-NET始终优于主流和紧凑型体系结构（例如Resnet，vgg，vgg，EfficityNet，darts和mobilenet）下最新的二进制方法。此外，我们在现实世界中的资源有限设备上执行DIR-NET，该设备可实现11.1倍的存储空间和5.4倍的速度。

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.

本文研究了重量和激活都将二进制神经网络（BNN）二进制为1位值，从而大大降低了记忆使用率和计算复杂性。由于现代深层神经网络具有复杂的设计，具有复杂的架构，其准确性，因此权重和激活分布的多样性非常高。因此，传统的符号函数不能很好地用于有效地在BNN中进行全精度值。为此，我们提出了一种称为Adabin的简单而有效的方法，可自适应获得最佳的二进制集$ \ {b_1，b_2 \} $（$ b_1，b_1，b_2 \ in \ mathbb {r} $）的重量和激活而不是固定集（即$ \ { - 1，+1 \} $）。通过这种方式，提出的方法可以更好地拟合不同的分布，并提高二进制特征的表示能力。实际上，我们使用中心位置和1位值的距离来定义新的二进制量化函数。对于权重，我们提出了一种均衡方法，将对称分布的对称中心与实价分布相对，并最大程度地减少它们的kullback-leibler差异。同时，我们引入了一种基于梯度的优化方法，以获取这两个激活参数，这些参数以端到端的方式共同训练。基准模型和数据集的实验结果表明，拟议的Adabin能够实现最新性能。例如，我们使用RESNET-18体系结构在Imagenet上获得66.4 \％TOP-1的精度，并使用SSD300获得了Pascal VOC的69.4映射。

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。

Adder Neural Network (AdderNet) provides a new way for developing energy-efficient neural networks by replacing the expensive multiplications in convolution with cheaper additions (i.e.l1-norm). To achieve higher hardware efficiency, it is necessary to further study the low-bit quantization of AdderNet. Due to the limitation that the commutative law in multiplication does not hold in l1-norm, the well-established quantization methods on convolutional networks cannot be applied on AdderNets. Thus, the existing AdderNet quantization techniques propose to use only one shared scale to quantize both the weights and activations simultaneously. Admittedly, such an approach can keep the commutative law in the l1-norm quantization process, while the accuracy drop after low-bit quantization cannot be ignored. To this end, we first thoroughly analyze the difference on distributions of weights and activations in AdderNet and then propose a new quantization algorithm by redistributing the weights and the activations. Specifically, the pre-trained full-precision weights in different kernels are clustered into different groups, then the intra-group sharing and inter-group independent scales can be adopted. To further compensate the accuracy drop caused by the distribution difference, we then develop a lossless range clamp scheme for weights and a simple yet effective outliers clamp strategy for activations. Thus, the functionality of full-precision weights and the representation ability of full-precision activations can be fully preserved. The effectiveness of the proposed quantization method for AdderNet is well verified on several benchmarks, e.g., our 4-bit post-training quantized adder ResNet-18 achieves an 66.5% top-1 accuracy on the ImageNet with comparable energy efficiency, which is about 8.5% higher than that of the previous AdderNet quantization methods.

内存处理（PIM）是一种越来越多地研究的神经形态硬件，承诺能量和吞吐量改进以进行深度学习推断。 PIM利用大量平行，有效的模拟计算在内存内部，绕过传统数字硬件中数据移动的瓶颈。但是，需要额外的量化步骤（即PIM量化），通常由于硬件约束而导致的分辨率有限，才能将模拟计算结果转换为数字域。同时，由于不完善的类似物到数字界面，PIM量化中的非理想效应广泛存在，这进一步损害了推理的准确性。在本文中，我们提出了一种培训量化网络的方法，以合并PIM量化，这对所有PIM系统无处不在。具体而言，我们提出了PIM量化意识培训（PIM-QAT）算法，并通过分析训练动力学以促进训练收敛，从而在向后传播期间引入重新传播技术。我们还提出了两种技术，即批处理归一化（BN）校准和调整精度训练，以抑制实际PIM芯片中涉及的非理想线性和随机热噪声的不利影响。我们的方法在三个主流PIM分解方案上进行了验证，并在原型芯片上进行了物理上的验证。与直接在PIM系统上部署常规训练的量化模型相比，该模型没有考虑到此额外的量化步骤并因此失败，我们的方法提供了重大改进。它还可以在CIFAR10和CIFAR100数据集上使用各种网络深度来获得最受欢迎的网络拓扑结构，在CIFAR10和CIFAR100数据集上，在PIM系统上达到了可比的推理精度。

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.

In deep neural networks (DNNs), there are a huge number of weights and multiply-and-accumulate (MAC) operations. Accordingly, it is challenging to apply DNNs on resource-constrained platforms, e.g., mobile phones. Quantization is a method to reduce the size and the computational complexity of DNNs. Existing quantization methods either require hardware overhead to achieve a non-uniform quantization or focus on model-wise and layer-wise uniform quantization, which are not as fine-grained as filter-wise quantization. In this paper, we propose a class-based quantization method to determine the minimum number of quantization bits for each filter or neuron in DNNs individually. In the proposed method, the importance score of each filter or neuron with respect to the number of classes in the dataset is first evaluated. The larger the score is, the more important the filter or neuron is and thus the larger the number of quantization bits should be. Afterwards, a search algorithm is adopted to exploit the different importance of filters and neurons to determine the number of quantization bits of each filter or neuron. Experimental results demonstrate that the proposed method can maintain the inference accuracy with low bit-width quantization. Given the same number of quantization bits, the proposed method can also achieve a better inference accuracy than the existing methods.

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。

Quantization has become a predominant approach for model compression, enabling deployment of large models trained on GPUs onto smaller form-factor devices for inference. Quantization-aware training (QAT) optimizes model parameters with respect to the end task while simulating quantization error, leading to better performance than post-training quantization. Approximation of gradients through the non-differentiable quantization operator is typically achieved using the straight-through estimator (STE) or additive noise. However, STE-based methods suffer from instability due to biased gradients, whereas existing noise-based methods cannot reduce the resulting variance. In this work, we incorporate exponentially decaying quantization-error-aware noise together with a learnable scale of task loss gradient to approximate the effect of a quantization operator. We show this method combines gradient scale and quantization noise in a better optimized way, providing finer-grained estimation of gradients at each weight and activation layer's quantizer bin size. Our controlled noise also contains an implicit curvature term that could encourage flatter minima, which we show is indeed the case in our experiments. Experiments training ResNet architectures on the CIFAR-10, CIFAR-100 and ImageNet benchmarks show that our method obtains state-of-the-art top-1 classification accuracy for uniform (non mixed-precision) quantization, out-performing previous methods by 0.5-1.2% absolute.

深神经网络（DNN）的庞大计算和记忆成本通常排除了它们在资源约束设备中的使用。将参数和操作量化为较低的位精确，为神经网络推断提供了可观的记忆和能量节省，从而促进了在边缘计算平台上使用DNN。量化DNN的最新努力采用了一系列技术，包括渐进式量化，步进尺寸的适应性和梯度缩放。本文提出了一种针对边缘计算的混合精度卷积神经网络（CNN）的新量化方法。我们的方法在模型准确性和内存足迹上建立了一个新的Pareto前沿，展示了一系列量化模型，可提供低于4.3 MB的权重（WGTS。）和激活（ACTS。）。我们的主要贡献是：（i）用张量学的学习精度，（ii）WGTS的靶向梯度修饰，（i）硬件感知的异质可区分量化。和行为。为了减轻量化错误，以及（iii）多相学习时间表，以解决从更新到学习的量化器和模型参数引起的学习不稳定性。我们证明了我们的技术在Imagenet数据集上的有效性，包括高效网络lite0（例如，WGTS。的4.14MB和ACTS。以67.66％的精度）和MobilenEtV2（例如3.51MB WGTS。 ％ 准确性）。

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。

已经证明量化是提高深神经网络推理效率的重要方法（DNN）。然而，在将DNN权重或从高精度格式从高精度格式量化到它们量化的对应物的同时，在准确性和效率之间取得良好的平衡仍然具有挑战性。我们提出了一种称为弹性显着位量化（ESB）的新方法，可控制量化值的有效位数，以获得具有更少资源的更好的推理准确性。我们设计一个统一的数学公式，以限制ESB的量化值，具有灵活的有效位。我们还引入了分布差对准器（DDA），以定量对齐全精密重量或激活值和量化值之间的分布。因此，ESB适用于各种重量和DNN的激活的各种钟形分布，从而保持高推理精度。从较少的量化值中受益于较少的量化值，ESB可以降低乘法复杂性。我们将ESB实施为加速器，并定量评估其对FPGA的效率。广泛的实验结果表明，ESB量化始终如一地优于最先进的方法，并分别通过AlexNet，Resnet18和MobileNetv2的平均精度提高4.78％，1.92％和3.56％。此外，ESB作为加速器可以在Xilinx ZCU102 FPGA平台上实现1K LUT的10.95 GOPS峰值性能。与FPGA上的CPU，GPU和最先进的加速器相比，ESB加速器可以分别将能效分别提高到65倍，11x和26倍。

模型量化已成为加速深度学习推理的不可或缺的技术。虽然研究人员继续推动量化算法的前沿，但是现有量化工作通常是不可否认的和不可推销的。这是因为研究人员不选择一致的训练管道并忽略硬件部署的要求。在这项工作中，我们提出了模型量化基准（MQBench），首次尝试评估，分析和基准模型量化算法的再现性和部署性。我们为实际部署选择多个不同的平台，包括CPU，GPU，ASIC，DSP，并在统一培训管道下评估广泛的最新量化算法。 MQBENCK就像一个连接算法和硬件的桥梁。我们进行全面的分析，并找到相当大的直观或反向直观的见解。通过对齐训练设置，我们发现现有的算法在传统的学术轨道上具有大致相同的性能。虽然用于硬件可部署量化，但有一个巨大的精度差距，仍然不稳定。令人惊讶的是，没有现有的算法在MQBench中赢得每一项挑战，我们希望这项工作能够激发未来的研究方向。

当通过模拟量化训练神经网络时，我们观察到，量化的权重可以意外地在两个网格点之间振荡。这种效果的重要性及其对量化感知培训（QAT）的影响并未在文献中得到充分理解或研究。在本文中，我们更深入地研究了重量振荡现象，并表明由于推理过程中错误估计的批次纳入统计量和训练期间的噪声增加，它可能导致明显的准确性降解。这些效果在低位（$ \ leq $ 4位）的高效网络中尤其明显，具有深度可分开的层，例如mobilenets和效率网络。在我们的分析中，我们研究了一些先前提出的QAT算法，并表明其中大多数无法克服振荡。最后，我们提出了两种新型的QAT算法来克服训练期间的振荡：振荡衰减和迭代重量冻结。我们证明，我们的算法对于低位（3＆4位）的重量（3＆4位）的最新精度以及有效体系结构的激活量化，例如MobilenetV2，MobilenetV3和Imagenet上的EfficentNet-Lite。我们的源代码可在{https://github.com/qualcomm-ai-research/oscillations-qat}上获得。

在资源受限的嵌入式系统上部署卷积神经网络的关键推动力是二进制神经网络（BNN）。 BNNS通过将功能和权重进行分配来保存内存并简化计算。不幸的是，二进制不可避免地伴随着准确性的严重降低。为了减少二进制和完整精确网络之间的准确性差距，最近提出了许多维修方法，我们已经将其分类并在本章中进行了单一概述。维修方法分为两个主要分支，培训技术和网络拓扑变化，可以进一步分为较小的类别。后一个类别为嵌入式系统引入了额外的成本（能源消耗或额外的面积），而前者则没有。从我们的概述中，我们可以观察到在减少准确性差距方面取得了进展，但是BNN论文并不对应使用哪种修复方法进行对齐，以获得高度准确的BNN。因此，本章包含一项经验综述，该综述评估了许多维修方法的好处，而不是Resnet-20 \＆Cifar10和Resnet-18 \＆Cifar100基准。我们发现三个维修类别最有益：功能二进制器，功能归一化和双重残留。基于这篇评论，我们讨论未来的方向和研究机会。我们勾勒出与BNN在嵌入式系统上相关的收益和成本，因为BNN是否能够缩小准确性差距，同时在资源受限的嵌入式系统上保持高能效率仍然有待观察。