In recent years, image and video delivery systems have begun integrating deep learning super-resolution (SR) approaches, leveraging their unprecedented visual enhancement capabilities while reducing reliance on networking conditions. Nevertheless, deploying these solutions on mobile devices still remains an active challenge as SR models are excessively demanding with respect to workload and memory footprint. Despite recent progress on on-device SR frameworks, existing systems either penalize visual quality, lead to excessive energy consumption or make inefficient use of the available resources. This work presents NAWQ-SR, a novel framework for the efficient on-device execution of SR models. Through a novel hybrid-precision quantization technique and a runtime neural image codec, NAWQ-SR exploits the multi-precision capabilities of modern mobile NPUs in order to minimize latency, while meeting user-specified quality constraints. Moreover, NAWQ-SR selectively adapts the arithmetic precision at run time to equip the SR DNN's layers with wider representational power, improving visual quality beyond what was previously possible on NPUs. Altogether, NAWQ-SR achieves an average speedup of 7.9x, 3x and 1.91x over the state-of-the-art on-device SR systems that use heterogeneous processors (MobiSR), CPU (SplitSR) and NPU (XLSR), respectively. Furthermore, NAWQ-SR delivers an average of 3.2x speedup and 0.39 dB higher PSNR over status-quo INT8 NPU designs, but most importantly mitigates the negative effects of quantization on visual quality, setting a new state-of-the-art in the attainable quality of NPU-based SR.

Recent image degradation estimation methods have enabled single-image super-resolution (SR) approaches to better upsample real-world images. Among these methods, explicit kernel estimation approaches have demonstrated unprecedented performance at handling unknown degradations. Nonetheless, a number of limitations constrain their efficacy when used by downstream SR models. Specifically, this family of methods yields i) excessive inference time due to long per-image adaptation times and ii) inferior image fidelity due to kernel mismatch. In this work, we introduce a learning-to-learn approach that meta-learns from the information contained in a distribution of images, thereby enabling significantly faster adaptation to new images with substantially improved performance in both kernel estimation and image fidelity. Specifically, we meta-train a kernel-generating GAN, named MetaKernelGAN, on a range of tasks, such that when a new image is presented, the generator starts from an informed kernel estimate and the discriminator starts with a strong capability to distinguish between patch distributions. Compared with state-of-the-art methods, our experiments show that MetaKernelGAN better estimates the magnitude and covariance of the kernel, leading to state-of-the-art blind SR results within a similar computational regime when combined with a non-blind SR model. Through supervised learning of an unsupervised learner, our method maintains the generalizability of the unsupervised learner, improves the optimization stability of kernel estimation, and hence image adaptation, and leads to a faster inference with a speedup between 14.24 to 102.1x over existing methods.

随着深度神经网络（DNN）的出现，成为许多计算机视觉任务中的骨干，它们在现实世界中的消费应用程序中的采用不断扩大。鉴于智能设备的丰富性和无所不能，正在形成“智能生态系统”，同时进行感应而不是独立。这将处式推理范式转移到在边缘部署集中式神经加工单元（NPU），其中多个设备（例如，在智能家居或自动驾驶汽车中）可以通过动态速率流式传输数据以进行处理。尽管这为输入批处理提供了增强的潜力，但幼稚的解决方案可以导致表现不佳的性能和经验质量，尤其是在尖峰负载下。同时，动态DNN的部署，包括随机计算图（例如早期 - 外观（EE）模型），引入了此类系统中动态行为的新维度。在这项工作中，我们提出了一种新颖的早期感知的调度算法，该算法允许在运行时进行样本抢占，以说明到达和早期外来过程引入的动态性。同时，我们向NPU硬件体系结构的设计空间介绍了两个新颖的维度，即流体批处理和可堆叠的处理元素，这些元素可以使运行时适应性适应不同的批次尺寸，并显着改善了NPU利用率，即使在小批次尺寸下也是如此。我们的评估表明，我们的系统分别在平均延迟和尾部潜伏期SLO满意度方面，平均达到1.97倍和6.7倍的改善。

基于注意力的神经网络在许多AI任务中都普遍存在。尽管其出色的算法性能，但注意力机制和前馈网络（FFN）的使用仍需要过多的计算和内存资源，这通常会损害其硬件性能。尽管已经引入了各种稀疏变体，但大多数方法仅着重于缓解算法级别上的二次注意力缩放，而无需明确考虑将其方法映射到真实硬件设计上的效率。此外，大多数努力仅专注于注意机制或FFN，但没有共同优化这两个部分，导致当前的大多数设计在处理不同的输入长度时缺乏可扩展性。本文从硬件角度系统地考虑了不同变体中的稀疏模式。在算法级别上，我们提出了Fabnet，这是一种适合硬件的变体，它采用统一的蝴蝶稀疏模式来近似关注机制和FFN。在硬件级别上，提出了一种新颖的适应性蝴蝶加速器，可以在运行时通过专用硬件控件配置，以使用单个统一的硬件引擎加速不同的蝴蝶层。在远程 - ARENA数据集上，FabNet达到了与香草变压器相同的精度，同时将计算量减少10到66次，参数数量为2至22次。通过共同优化算法和硬件，我们的基于FPGA的蝴蝶加速器在归一化到同一计算预算的最新加速器上达到了14.2至23.2倍的速度。与Raspberry Pi 4和Jetson Nano上优化的CPU和GPU设计相比，我们的系统在相同的功率预算下的最大273.8和15.1倍。

语义细分是许多视觉系统的骨干，从自动驾驶汽车和机器人导航到增强现实和电信。在有限的资源信封内经常在严格的延迟约束下运行，对有效执行的优化变得很重要。同时，目标平台的异质功能以及不同应用程序的不同限制需要设计和培训多个针对特定目标的细分模型，从而导致过度维护成本。为此，我们提出了一个框架，用于将最新的分割CNN转换为多EXIT语义细分（MESS）网络：经过特殊训练的模型，这些模型沿其深度沿其深度进行参数化的早期出口到i）在推断过程中动态保存计算更容易的样本和ii）通过提供可定制的速度准确性权衡来节省培训和维护成本。设计和培训此类网络天真地损害了性能。因此，我们为多EXIT网络提出了新颖的两期培训方案。此外，Mess的参数化可以使附件分割头的数字，位置和体系结构以及退出策略通过详尽的搜索在<1GPUH中进行部署。这使得混乱能够快速适应每个目标用例的设备功能和应用要求，并提供火车一路上的部署解决方案。与原始的骨干网络相比，Mess变体具有相同精度的潜伏期增长率高达2.83倍，而相同的计算预算的潜伏期提高到同一计算预算的准确性高5.33 pp。最后，与最先进的技术相比，MESS提供了更快的架构选择订单。

最近，使用卷积神经网络（CNNS）存在移动和嵌入式应用的爆炸性增长。为了减轻其过度的计算需求，开发人员传统上揭示了云卸载，突出了高基础设施成本以及对网络条件的强烈依赖。另一方面，强大的SOC的出现逐渐启用设备执行。尽管如此，低端和中层平台仍然努力充分运行最先进的CNN。在本文中，我们展示了Dyno，一种分布式推断框架，将两全其人的最佳框架结合起来解决了几个挑战，例如设备异质性，不同的带宽和多目标要求。启用这是其新的CNN特定数据包装方法，其在onloading计算时利用CNN的不同部分的精度需求的可变性以及其新颖的调度器，该调度器共同调谐分区点并在运行时传输数据精度适应其执行环境的推理。定量评估表明，Dyno优于当前最先进的，通过竞争对手的CNN卸载系统，在竞争对手的CNN卸载系统上提高吞吐量超过一个数量级，最高可达60倍的数据。

联邦学习（FL）一直在不同的ML任务中获得显着的牵引力，从视野到键盘预测。在大规模的部署中，客户异质性是一个事实，并构成公平，培训性能和准确性的主要问题。虽然已经进行了统计数据异质性的重大努力，但是作为系统异质性称为客户端的处理能力和网络带宽的多样性仍然很大程度上是未开发的。当前解决方案无论是忽略大部分可用的设备，也无限制地设定均匀限制，由最低能力的参与者限制。在这项工作中，我们介绍了有序的辍学，这是一种机制，实现了深度神经网络（DNN）中的有序，嵌套的知识表示，并且能够在不需要再培训的情况下提取较低的脚印子模型。我们进一步表明，对于线性地图，我们的订购辍学等同于SVD。我们采用这种技术，以及一种自蒸馏方法，在一个叫做峡湾的框架中。 Fjord通过将模型宽度定制到客户端的功能来减轻客户体系异质性的问题。在各种方式上对CNN和RNN的广泛评估表明，峡湾始终如一地导致最先进的基线的显着性能，同时保持其嵌套结构。

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regression. In this study, we get NNs that generate highly accurate and highly smooth function, which only comprised of a few weight parameters, through discussing a few topics about regression. First, we reinterpret inside of NNs for regression; consequently, we propose a new activation function--integrated sigmoid linear unit (ISLU). Then special charateristics of metadata for regression, which is different from other data like image or sound, is discussed for improving the performance of neural networks. Finally, the one of a simple hierarchical NN that generate models substituting mathematical function is presented, and the new batch concept ``meta-batch" which improves the performance of NN several times more is introduced. The new activation function, meta-batch method, features of numerical data, meta-augmentation with metaparameters, and a structure of NN generating a compact multi-layer perceptron(MLP) are essential in this study.

We present a novel dataset named as HPointLoc, specially designed for exploring capabilities of visual place recognition in indoor environment and loop detection in simultaneous localization and mapping. The loop detection sub-task is especially relevant when a robot with an on-board RGB-D camera can drive past the same place (``Point") at different angles. The dataset is based on the popular Habitat simulator, in which it is possible to generate photorealistic indoor scenes using both own sensor data and open datasets, such as Matterport3D. To study the main stages of solving the place recognition problem on the HPointLoc dataset, we proposed a new modular approach named as PNTR. It first performs an image retrieval with the Patch-NetVLAD method, then extracts keypoints and matches them using R2D2, LoFTR or SuperPoint with SuperGlue, and finally performs a camera pose optimization step with TEASER++. Such a solution to the place recognition problem has not been previously studied in existing publications. The PNTR approach has shown the best quality metrics on the HPointLoc dataset and has a high potential for real use in localization systems for unmanned vehicles. The proposed dataset and framework are publicly available: https://github.com/metra4ok/HPointLoc.