Distributed training enables large-scale deep learning, but suffers from high communication overhead, especially as models and datasets grow. Gradient compression, particularly quantization, is a promising approach to mitigate this bottleneck. However, existing quantization schemes are often incompatible with Allreduce, the dominant communication primitive in distributed deep learning, and many prior solutions rely on heuristics without theoretical guarantees. We introduce Global-QSGD, an Allreduce-compatible gradient quantization method that leverages global norm scaling to reduce communication overhead while preserving accuracy. Global-QSGD is backed by rigorous theoretical analysis, extending standard unbiased compressor frameworks to establish formal convergence guarantees. Additionally, we develop a performance model to evaluate its impact across different hardware configurations. Extensive experiments on NVLink, PCIe, and large-scale cloud environments show that Global-QSGD accelerates distributed training by up to 3.51× over baseline quantization methods, making it a practical and efficient solution for large-scale deep learning workloads.

The continuous growth of on-chip transistors driven by technology scaling urges architecture developers to design and implement novel architectures to effectively utilize the excessive on-chip resources.Due to the challenges of programming in register-transfer level (RTL) languages, performance modeling based on simulation is typically developed alongside hardware implementation, allowing the exploration of high-level design decisions before dealing with the error-prone, low-level RTL details.However, this approach also introduces new challenges in coordinating across multiple teams to align implementation details separate codebases.In this paper, we address this issue by presenting Assassyn, a unified, high-level, and general-purpose programming framework for architectural simulation and implementation.By taking advantage of the concept of asynchronous event handling, a widely existing behavior in both hardware design and implementation and software engineering, a general-purpose, and high-level programming abstraction is proposed to mitigate the difficulties of RTL programming.Moreover, the unified programming interface naturally enables an accurate and faithful alignment between the simulation-based performance modeling and RTL implementation.Our evaluation demonstrates that Assassyn's high-level programming interface is sufficiently expressive to implement a wide range * Serve as both the first and correspondence author.

Synthetic aperture passive positioning (SAPP) has gradually become a research hotspot in the field of single-station passive positioning. Except for the acoustic band, almost all research in the microwave band only focuses on the spaceborne scenarios. In this paper, we attempt to extend SAPP to more scenarios, such as airborne and vehicle-mounted applications, and introduce the motion error problem. The difficulty is that, different from the problem of SAR motion compensation, the target area of passive positioning is unknown, and the compensation matrix cannot be calculated directly. We propose an iterative motion compensation (IMC) algorithm to solve it. Both simulation and vehicle experiment results verify the effectiveness of the IMC algorithm. The results show that IMC improves the positioning accuracy by two orders of magnitude in the presence of motion error and exhibits strong convergence.

The recent surge in the prosperity of the Internet of Things (IoT) has been attracting an increasing number of researchers and experts with great attention due to its significant economic and social values. The IoT brings appealing opportunities and new challenges for both the current and future Internet. In practice, various IoT smart devices are generally pre-programmed and deployed specifically in the proper place to fulfill corresponding functions according to divergent requirements. However, lately, these pre-stored functions tend to be upgraded or reprogrammed more frequently on account of the increment of dynamic needs or urgent situations. Inspired by Software Defined Networking (SDN), the authors propose a framework in this work: Software Defined Function (SDF) for IoT, enabling IoT smart devices to be upgraded or reprogrammed securely and remotely. The authors further present a protocol named as OpenFunction stemmed from OpenFlow. Moreover, the security properties of this protocol are analyzed. Finally, the authors implement a preliminary SDF system and evaluate its performance. Experimental results indicate that OpenFunction allows a controller to update or rewrite functions in IoT devices, as well as to obtain flexibility and security. Accordingly, this work contributes to the future fusion of SDN and IoT technologies.