arXiv subjects

Weiying Wang

Publications and source records attributed to Weiying Wang.

9 recordsLinked to original sources

arXiv2026

SEAM-V: A Hybrid-Decoupled RISC-V Vector Processor with Backend-Visible Packet Semantics and Source-Lifetime-Aware Scheduling

Data-parallel workloads in deep learning and scientific computing continue to increase the demands on processor throughput, energy efficiency, and scalability. The RISC-V Vector Extension (RVV) supports scalable execution through a vector-length-agnostic model, yet many tightly coupled implementations still rely on the scalar core to supply vector instructions individually and are therefore constrained by instruction supply, scalar-side progress, memory stalls, and conservative dependence management in short-vector, loop-tail, and control/memory-interleaved scenarios. This paper presents SEAM-V, a hybrid-decoupled RVV processor that uses task-level decoupling, local instruction supply, and VLIW-style packing to form a continuous stream of execute packets (EPs). During EP formation and request serialization, SEAM-V preserves the association between prefetch intent and the corresponding load to support request-bound prefetching, while lane-level source-read completion is used to release pure write-after-read (WAR) dependences early; other dependences remain governed by conventional mechanisms. Relative to an Ara-based tightly coupled baseline (TC), SEAM-V achieves a geometric mean speedup of 1.38x across 17 representative kernel configurations. The one-dimensional vector, BLAS and matrix, and fixed-size application workload groups achieve 1.56x, 1.35x, and 1.23x, respectively. Synthesis and power analysis show that SEAM-V increases total cell area by 4.29% and geometric mean runtime power by 17.30%, while reducing task energy by 12.70%, demonstrating improved sustained execution efficiency and task-level energy efficiency with limited area overhead.

cs.AR↗

arXiv2026

Microarchitectural Co-Optimization for Sustained Throughput of RISC-V Multi-Lane Chaining Vector Processors

Modern RISC vector processors rely on multi-lane parallelism and chaining to achieve high sustained throughput, yet practical execution often deviates from the ideal reference due to microarchitectural inefficiencies. This work targets the open-source RVV processor Ara and analyzes its sustained-throughput loss under a fixed hardware configuration. We first establish an ideal multi-lane chaining model that decomposes ideal execution into prologue startup, steady-state progression, and tail drain, and uses this reference to characterize real-execution deviations. Based on this model, we attribute Ara's bottlenecks to three critical paths: memory-side data supply and transaction progression, dependence-and-issue control, and operand delivery and result propagation. To address these bottlenecks, we propose coordinated optimizations, including a descriptor-driven memory front end with next-VL prefetch, early read-dependence release with dynamic local issue control, and multi-source forwarding with dual-source operand queues. Experimental results show that, without increasing raw memory bandwidth or changing the main processor configuration, Ara-Opt achieves a geometric-mean speedup of 1.33x over baseline Ara. Under roofline-based normalization, the geometric-mean gap-closed ratio reaches 12.2%. In particular, scal, axpy, ger, and gemm achieve speedups of approximately 2.41x, 1.60x, 1.52x, and 1.42x, with corresponding gap-closed ratios of 93.7%, 88.9%, 78.3%, and 59.3%, respectively. These results show that the proposed optimizations recover lost sustained throughput under essentially unchanged hardware resources and move regular streaming and high-throughput workloads closer to the roofline-based performance bound.

cs.AR↗

arXiv2024

MULAN-WC: Multi-Robot Localization Uncertainty-aware Active NeRF with Wireless Coordination

This paper presents MULAN-WC, a novel multi-robot 3D reconstruction framework that leverages wireless signal-based coordination between robots and Neural Radiance Fields (NeRF). Our approach addresses key challenges in multi-robot 3D reconstruction, including inter-robot pose estimation, localization uncertainty quantification, and active best-next-view selection. We introduce a method for using wireless Angle-of-Arrival (AoA) and ranging measurements to estimate relative poses between robots, as well as quantifying and incorporating the uncertainty embedded in the wireless localization of these pose estimates into the NeRF training loss to mitigate the impact of inaccurate camera poses. Furthermore, we propose an active view selection approach that accounts for robot pose uncertainty when determining the next-best views to improve the 3D reconstruction, enabling faster convergence through intelligent view selection. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our framework in theory and in practice. Leveraging wireless coordination and localization uncertainty-aware training, MULAN-WC can achieve high-quality 3d reconstruction which is close to applying the ground truth camera poses. Furthermore, the quantification of the information gain from a novel view enables consistent rendering quality improvement with incrementally captured images by commending the robot the novel view position. Our hardware experiments showcase the practicality of deploying MULAN-WC to real robotic systems.

cs.RO↗

arXiv2023

A wireless signal-based sensing framework for robotics

In this paper we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging robots' mobility. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots' local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and ii) a Cramer-Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOA estimation as a function of the geometry of robot motion. We show that allowing robots to use their full mobility in 3D space while performing SAR, results in more accurate AOA profiles and thus better AOA estimation. All analytical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms using 5 GHz WiFi. Our experimental results bolster our analytical findings, demonstrating that 3D motion provides enhanced and consistent accuracy, with total AOA error of less than 10 degree for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA.

cs.RO↗

arXiv2022

Wi-Closure: Reliable and Efficient Search of Inter-robot Loop Closures Using Wireless Sensing

In this paper we propose a novel algorithm, Wi-Closure, to improve computational efficiency and robustness of loop closure detection in multi-robot SLAM. Our approach decreases the computational overhead of classical approaches by pruning the search space of potential loop closures, prior to evaluation by a typical multi-robot SLAM pipeline. Wi-Closure achieves this by identifying candidates that are spatially close to each other by using sensing over the wireless communication signal between robots, even when they are operating in non-line-of-sight or in remote areas of the environment from one another. We demonstrate the validity of our approach in simulation and hardware experiments. Our results show that using Wi-closure greatly reduces computation time, by 54% in simulation and by 77% in hardware compared, with a multi-robot SLAM baseline. Importantly, this is achieved without sacrificing accuracy. Using Wi-Closure reduces absolute trajectory estimation error by 99% in simulation and 89.2% in hardware experiments. This improvement is due in part to Wi-Closure's ability to avoid catastrophic optimization failure that typically occurs with classical approaches in challenging repetitive environments.

cs.RO↗

arXiv2022

Image Difference Captioning with Pre-training and Contrastive Learning

The Image Difference Captioning (IDC) task aims to describe the visual differences between two similar images with natural language. The major challenges of this task lie in two aspects: 1) fine-grained visual differences that require learning stronger vision and language association and 2) high-cost of manual annotations that leads to limited supervised data. To address these challenges, we propose a new modeling framework following the pre-training-finetuning paradigm. Specifically, we design three self-supervised tasks and contrastive learning strategies to align visual differences and text descriptions at a fine-grained level. Moreover, we propose a data expansion strategy to utilize extra cross-task supervision information, such as data for fine-grained image classification, to alleviate the limitation of available supervised IDC data. Extensive experiments on two IDC benchmark datasets, CLEVR-Change and Birds-to-Words, demonstrate the effectiveness of the proposed modeling framework. The codes and models will be released at https://github.com/yaolinli/IDC.

cs.MM↗

arXiv2021

Toolbox Release: A WiFi-Based Relative Bearing Sensor for Robotics

This paper presents the WiFi-Sensor-for-Robotics (WSR) toolbox, an open source C++ framework. It enables robots in a team to obtain relative bearing to each other, even in non-line-of-sight (NLOS) settings which is a very challenging problem in robotics. It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment. This capability, based on the theory developed in our prior works, is made available for the first time as an opensource tool. It is motivated by the lack of easily deployable solutions that use robots' local resources (e.g WiFi) for sensing in NLOS. This has implications for localization, ad-hoc robot networks, and security in multi-robot teams, amongst others. The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors. We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings for a multi-robot localization usecase. Empirical results show that the bearing estimation from our toolbox achieves mean accuracy of 5.10 degrees. This leads to a median error of 0.5m and 0.9m for localization in LOS and NLOS settings respectively, in a hardware deployment in an indoor office environment.

cs.RO↗

arXiv2021

Active Rendezvous for Multi-Robot Pose Graph Optimization using Sensing over Wi-Fi

We present a novel framework for collaboration amongst a team of robots performing Pose Graph Optimization (PGO) that addresses two important challenges for multi-robot SLAM: i) that of enabling information exchange "on-demand" via Active Rendezvous without using a map or the robot's location, and ii) that of rejecting outlying measurements. Our key insight is to exploit relative position data present in the communication channel between robots to improve groundtruth accuracy of PGO. We develop an algorithmic and experimental framework for integrating Channel State Information (CSI) with multi-robot PGO; it is distributed, and applicable in low-lighting or featureless environments where traditional sensors often fail. We present extensive experimental results on actual robots and observe that using Active Rendezvous results in a 64% reduction in ground truth pose error and that using CSI observations to aid outlier rejection reduces ground truth pose error by 32%. These results show the potential of integrating communication as a novel sensor for SLAM.

cs.RO↗

arXiv2020

YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos

The goal of the YouMakeup VQA Challenge 2020 is to provide a common benchmark for fine-grained action understanding in domain-specific videos e.g. makeup instructional videos. We propose two novel question-answering tasks to evaluate models' fine-grained action understanding abilities. The first task is \textbf{Facial Image Ordering}, which aims to understand visual effects of different actions expressed in natural language to the facial object. The second task is \textbf{Step Ordering}, which aims to measure cross-modal semantic alignments between untrimmed videos and multi-sentence texts. In this paper, we present the challenge guidelines, the dataset used, and performances of baseline models on the two proposed tasks. The baseline codes and models are released at \url{https://github.com/AIM3-RUC/YouMakeup_Baseline}.

cs.CV↗