Energy harvesting creates an emerging intermittent computing paradigm, but poses new challenges for sophisticated applications such as intermittent deep neural network (DNN) inference. Although model compression has adapted DNNs to resource constrained devices, under intermittent power, compressed models will still experience multiple power failures during a single inference. Footprint-based approaches enable hardware accelerated intermittent DNN inference by tracking footprints, independent of model computations, to indicate accelerator progress across power cycles. However, we observe that the extra overhead required to preserve progress indicators can severely offset the computation progress accumulated by intermittent DNN inference. This work proposes the concept of model augmentation to adapt DNNs to intermittent devices. Our middleware stack, JAPARI, appends extra neural network components into a given DNN, to enable the accelerator to intrinsically integrate progress indicators into the inference process, without affecting model accuracy. Their specific positions allow progress indicator preservation to be piggybacked onto output feature preservation to amortize the extra overhead, and their assigned values ensure uniquely distinguishable progress indicators for correct inference recovery upon power resumption. Evaluations on a Texas Instruments device under various DNN models, capacitor sizes, and progress preservation granularities, show that JAPARI can speed up intermittent DNN inference by 3x over the state of the art, for common convolutional neural architectures that require heavy acceleration.
High-fidelity kinship face synthesis is a challenging task due to the limited amount of kinship data available for training and low-quality images. In addition, it is also hard to trace the genetic traits between parents and children from those low-quality training images. To address these issues, we leverage the pre-trained state-of-the-art face synthesis model, StyleGAN2, for kinship face synthesis. To handle large age, gender and other attribute variations between the parents and their children, we conduct a thorough study of its rich latent spaces and different encoder architectures for an optimized encoder design to repurpose StyleGAN2 for kinship face synthesis. The obtained latent representation from our developed encoder pipeline with stage-wise training strikes a better balance of editability and synthesis fidelity for identity preserving and attribute manipulations than other compared approaches. With extensive subjective, quantitative, and qualitative evaluations, the proposed approach consistently achieves better performance in terms of facial attribute heredity and image generation fidelity than other compared state-of-the-art methods. This demonstrates the effectiveness of the proposed approach which can yield promising and satisfactory kinship face synthesis using only a single and straightforward encoder architecture.
Deep neural network (DNN) inference on intermittently-powered battery-less devices has the potential to unlock new possibilities for sustainable and intelligent edge applications. Existing intermittent inference approaches preserve progress information separate from the computed output features during inference. However, we observe that even in highly specialized approaches, the additional overhead incurred for inference progress preservation still accounts for a significant portion of the inference latency. This work proposes the concept of stateful neural networks, which enables a DNN to indicate the inference progress itself. Our runtime middleware embeds state information into the DNN such that the computed and preserved output features intrinsically contain progress indicators, avoiding the need to preserve them separately. The specific position and representation of the embedded states jointly ensure both output features and states are not corrupted while maintaining model accuracy, and the embedded states allow the latest output feature to be determined, enabling correct inference recovery upon power resumption. Evaluations were conducted on different Texas Instruments devices under varied intermittent power strengths and network models. Compared to the state of the art, our approach can speed up intermittent inference by 1.3 to 5 times, achieving higher performance when executing modern convolutional networks with weaker power.
Internet-of-Things (IoT) devices are gradually adopting battery-less, energy harvesting solutions, thereby driving the development of an intermittent computing paradigm to accumulate computation progress across multiple power cycles. While many attempts have been made to enable standalone intermittent systems, little attention has focused on IoT networks formed by intermittent devices. We observe that the computation progress improved by \textit{distributed task concurrency} in an intermittent network can be significantly offset by data unavailability due to frequent system failures. This paper presents an intermittent-aware distributed concurrency control protocol which leverages existing data copies inherently created in the network to improve the computation progress of concurrently executed tasks. In particular, we propose a borrowing-based data management method to increase data availability and an intermittent two-phase commit procedure incorporated with distributed backward validation to ensure data consistency in the network. The proposed protocol was integrated into a FreeRTOS-extended intermittent operating system running on Texas Instruments devices. Experimental results show that the computation progress can be significantly improved, and this improvement is more apparent under weaker power, where more devices will remain offline for longer duration.
Mobile Edge Computing (MEC) is a promising technique in the 5G Era to improve the Quality of Experience (QoE) for online video streaming due to its ability to reduce the backhaul transmission by caching certain content. However, it still takes effort to address the user association and video quality selection problem under the limited resource of MEC to fully support the low-latency demand for live video streaming. We found the optimization problem to be a non-linear integer programming, which is impossible to obtain a globally optimal solution under polynomial time. In this paper, we formulate the problem and derive the closed-form solution in the form of Lagrangian multipliers; the searching of the optimal variables is formulated as a Multi-Arm Bandit (MAB) and we propose a Deep Deterministic Policy Gradient (DDPG) based algorithm exploiting the supply-demand interpretation of the Lagrange dual problem. Simulation results show that our proposed approach achieves significant QoE improvement, especially in the low wireless resource and high user number scenario compared to other baselines.
Beamforming is regarded as a promising technique for future wireless communication systems. In this regard, codebook-based beamforming offers satisfactory performance with acceptable computational complexity; however, it requires a high power-consumed beam overhead. To achieve balance between the utilized beam overhead and the achieved spectral efficiency performance, we propose a super-resolution-based scheme using a hierarchical codebook. We consider beam sweeping as an inference problem, where low-resolution beam radiating responses are used as an input, and high-resolution beam sweeping responses are output. Simulation results confirm that the proposed scheme exhibits extraordinary performance-overhead tradeoffs as compared with state-of-the-art codebook-based beamforming designs.
Vehicle positioning is a key component of autonomous driving. The global positioning system (GPS) is the most commonly used vehicle positioning system currently. However, its accuracy will be affected by environmental differences and thus fails to meet the requirements of meter-level accuracy. We consider a coordinate neighboring vehicle positioning system (CNVPS) based on GPS, omnidirectional radar, and V2V communication ability to obtain additional information from neighboring vehicles to improve the GPS positioning accuracy of vehicles in various environments. We further use the concept of transfer learning (TL) wherein an adversarial mechanism is designed to eliminate the deviation of multiple environments to optimize vehicle positioning accuracy in multiple environments using one model. The simulation results show that, compared with the existing methods, the proposed system architecture not only improves the performance but also effectively reduces the amount of data required for training.
Device-free wireless indoor localization is an essential technology for the Internet of Things (IoT), and fingerprint-based methods are widely used. A common challenge to fingerprint-based methods is data collection and labeling. This paper proposes a few-shot transfer learning system that uses only a small amount of labeled data from the current environment and reuses a large amount of existing labeled data previously collected in other environments, thereby significantly reducing the data collection and labeling cost for localization in each new environment. The core method lies in graph neural network (GNN) based few-shot transfer learning and its modifications. Experimental results conducted on real-world environments show that the proposed system achieves comparable performance to a convolutional neural network (CNN) model, with 40 times fewer labeled data.
Millimeter wave (mmWave) is a key technology for fifth-generation (5G) and beyond communications. Hybrid beamforming has been proposed for large-scale antenna systems in mmWave communications. Existing hybrid beamforming designs based on infinite-resolution phase shifters (PSs) are impractical due to hardware cost and power consumption. In this paper, we propose an unsupervised-learning-based scheme to jointly design the analog precoder and combiner with low-resolution PSs for multiuser multiple-input multiple-output (MU-MIMO) systems. We transform the analog precoder and combiner design problem into a phase classification problem and propose a generic neural network architecture, termed the phase classification network (PCNet), capable of producing solutions of various PS resolutions. Simulation results demonstrate the superior sum-rate and complexity performance of the proposed scheme, as compared to state-of-the-art hybrid beamforming designs for the most commonly used low-resolution PS configurations.
Device-free wireless indoor localization is a key enabling technology for the Internet of Things (IoT). Fingerprint-based indoor localization techniques are a commonly used solution. This paper proposes a semi-supervised, generative adversarial network (GAN)-based device-free fingerprinting indoor localization system. The proposed system uses a small amount of labeled data and a large amount of unlabeled data (i.e., semi-supervised), thus considerably reducing the expensive data labeling effort. Experimental results show that, as compared to the state-of-the-art supervised scheme, the proposed semi-supervised system achieves comparable performance with equal, sufficient amount of labeled data, and significantly superior performance with equal, highly limited amount of labeled data. Besides, the proposed semi-supervised system retains its performance over a broad range of the amount of labeled data. The interactions between the generator, discriminator, and classifier models of the proposed GAN-based system are visually examined and discussed. A mathematical description of the proposed system is also presented.