arXiv subjects

Yuhao Zhu

Publications and source records attributed to Yuhao Zhu.

At least 37 records · Page 2Linked to original sources

arXiv2025

A narrowband burst from FRB 20190520B simultaneously observed by FAST and Parkes

Fast Radio Bursts (FRBs) are short-duration radio transients with mysterious origins. Since its uncertainty, there are very few FRBs that are observed by different instruments, simultaneously. This study presents a detailed analysis of a burst from FRB 20190520B observed by FAST and Parkes at the same time. The spectrum of this individual burst ended at the upper limit of the FAST frequency band and was simultaneously detected by the Parkes telescope in the 1.5-1.8 GHz range. By employing spectral energy distribution (SED) and spectral sharpness methods, we confirmed the presence of narrowband radiation in FRB 20190520B, which is crucial for understanding its radiation mechanisms. Our findings support the narrowband characteristics that most repeaters exhibit. This work also highlights the necessity of continued multiband observations to explore its periodicity and frequency-dependent properties, contributing to an in-depth understanding of FRB phenomena.

astro-ph.HE↗

arXiv2025

Multi-year Polarimetric Monitoring of Four CHIME-Discovered Repeating Fast Radio Bursts with FAST

In this study, we report multi-year polarization measurements of four repeating FRBs initially discovered by CHIME: FRBs~20190117A, 20190208A, 20190303A, and 20190417A. We observed the four repeating FRBs with FAST, detecting a total of 66 bursts. Two bursts from FRB~20190417A exhibit a circular polarization signal-to-noise ratio greater than 7, with the highest circular polarization fraction recorded at 35.7%. While the bursts from FRBs 20190208A and 20190303A are highly linearly polarized, those from FRBs~20190117A and 20190417A show depolarization due to multi-path propagation, with σ_{\mathrm{RM}} = 2.78 \pm 0.05 rad m$^{-2}$ and 5.19 \pm 0.09 rad m$^{-2}$, respectively. The linear polarization distributions among five repeating FRB--FRBs~20190208A, 20190303A, 20201124A, 20220912A, and 20240114A--are nearly identical but show distinct differences from those of non-repeating FRBs. FRBs~20190117A, 20190303A, and 20190417A exhibit substantial rotation measure (RM) variations between bursts, joining other repeating FRBs in this behavior. Combining these findings with published results, 64% of repeating FRBs show RM variations greater than 50 rad m$^{-2}$, and 21\% exhibit RM reversals. A significant proportion of repeating FRBs reside in a dynamic magneto-ionic environment. The structure function of RM variations shows a power-law index of $γ\sim (0-0.8)$, corresponding to a shallow power spectrum $α= -(γ+ 2) \sim -(2.0-2.8)$ of turbulence, if the RM variations are attributed to turbulence. This suggests that the variations are dominated by small-scale RM density fluctuations. We perform K-S tests comparing the RMs of repeating and non-repeating FRBs, which reveal a marginal dichotomy in the distribution of their RMs.We caution that the observed dichotomy may be due to the small sample size and selection biases.

astro-ph.HE↗

arXiv2025

StreamGrid: Streaming Point Cloud Analytics via Compulsory Splitting and Deterministic Termination

Point clouds are increasingly important in intelligent applications, but frequent off-chip memory traffic in accelerators causes pipeline stalls and leads to high energy consumption. While conventional line buffer techniques can eliminate off-chip traffic, they cannot be directly applied to point clouds due to their inherent computation patterns. To address this, we introduce two techniques: compulsory splitting and deterministic termination, enabling fully-streaming processing. We further propose StreamGrid, a framework that integrates these techniques and automatically optimizes on-chip buffer sizes. Our evaluation shows StreamGrid reduces on-chip memory by 61.3\% and energy consumption by 40.5\% with marginal accuracy loss compared to the baselines without our techniques. Additionally, we achieve 10.0$\times$ speedup and 3.9$\times$ energy efficiency over state-of-the-art accelerators.

cs.AR↗

arXiv2025

VQ-LLM: High-performance Code Generation for Vector Quantization Augmented LLM Inference

In this work, we design and implement VQ-LLM, an efficient fused Vector Quantization (VQ) kernel generation framework. We first introduce a software abstraction called codebook cache to optimize codebook access efficiency and support the integration of VQ with various computations. The codebook cache adaptively stores different entries across the GPU's memory hierarchy, including off-chip global memory, on-chip shared memory, and registers. Centered around the codebook cache, we design an efficient computation engine that optimizes memory traffic during computations involving codebooks. This compute engine adopts the codebook-centric dataflow and fusion optimizations. Additionally, we provide adaptive heuristics to tailor parameter selection in our optimizations to diverse VQ configurations. Our optimizations achieve an average latency reduction of 46.13% compared to unoptimized versions. Compared to existing open-source implementations, our methods decrease latency by 64.36% to 99.1%. A final comparison with state-of-the-art element-wise quantization methods like AWQ and KVQuant shows that our VQ-LLM is practically viable, achieving latencies close or even better latencies to those at equivalent bit-widths, potentially offering greater accuracy.

cs.DC↗

arXiv2025

Dynamic Sparse Training of Diagonally Sparse Networks

Recent advances in Dynamic Sparse Training (DST) have pushed the frontier of sparse neural network training in structured and unstructured contexts, matching dense-model performance while drastically reducing parameter counts to facilitate model scaling. However, unstructured sparsity often fails to translate into practical speedups on modern hardware. To address this shortcoming, we propose DynaDiag, a novel structured sparse-to-sparse DST method that performs at par with unstructured sparsity. DynaDiag enforces a diagonal sparsity pattern throughout training and preserves sparse computation in forward and backward passes. We further leverage the diagonal structure to accelerate computation via a custom CUDA kernel, rendering the method hardware-friendly. Empirical evaluations on diverse neural architectures demonstrate that our method maintains accuracy on par with unstructured counterparts while benefiting from tangible computational gains. Notably, with 90% sparse linear layers in ViTs, we observe up to a 3.13x speedup in online inference without sacrificing model performance and a 1.59x speedup in training on a GPU compared to equivalent unstructured layers. Our source code is available at https://github.com/horizon-research/DynaDiag/.

cs.LG↗

arXiv2025

Lumina: Real-Time Mobile Neural Rendering by Exploiting Computational Redundancy

3D Gaussian Splatting (3DGS) has vastly advanced the pace of neural rendering, but it remains computationally demanding on today's mobile SoCs. To address this challenge, we propose Lumina, a hardware-algorithm co-designed system, which integrates two principal optimizations: a novel algorithm, S^2, and a radiance caching mechanism, RC, to improve the efficiency of neural rendering. S2 algorithm exploits temporal coherence in rendering to reduce the computational overhead, while RC leverages the color integration process of 3DGS to decrease the frequency of intensive rasterization computations. Coupled with these techniques, we propose an accelerator architecture, LuminCore, to further accelerate cache lookup and address the fundamental inefficiencies in Rasterization. We show that Lumina achieves 4.5x speedup and 5.3x energy reduction against a mobile Volta GPU, with a marginal quality loss (< 0.2 dB peak signal-to-noise ratio reduction) across synthetic and real-world datasets.

cs.AR↗

arXiv2025

SnapPix: Efficient-Coding--Inspired In-Sensor Compression for Edge Vision

Energy-efficient image acquisition on the edge is crucial for enabling remote sensing applications where the sensor node has weak compute capabilities and must transmit data to a remote server/cloud for processing. To reduce the edge energy consumption, this paper proposes a sensor-algorithm co-designed system called SnapPix, which compresses raw pixels in the analog domain inside the sensor. We use coded exposure (CE) as the in-sensor compression strategy as it offers the flexibility to sample, i.e., selectively expose pixels, both spatially and temporally. SNAPPIX has three contributions. First, we propose a task-agnostic strategy to learn the sampling/exposure pattern based on the classic theory of efficient coding. Second, we co-design the downstream vision model with the exposure pattern to address the pixel-level non-uniformity unique to CE-compressed images. Finally, we propose lightweight augmentations to the image sensor hardware to support our in-sensor CE compression. Evaluating on action recognition and video reconstruction, SnapPix outperforms state-of-the-art video-based methods at the same speed while reducing the energy by up to 15.4x. We have open-sourced the code at: https://github.com/horizon-research/SnapPix.

cs.CV↗

arXiv2024

MetaSapiens: Real-Time Neural Rendering with Efficiency-Aware Pruning and Accelerated Foveated Rendering

Point-Based Neural Rendering (PBNR) is emerging as a promising class of rendering techniques, which are permeating all aspects of society, driven by a growing demand for real-time, photorealistic rendering in AR/VR and digital twins. Achieving real-time PBNR on mobile devices is challenging. This paper proposes MetaSapiens, a PBNR system that for the first time delivers real-time neural rendering on mobile devices while maintaining human visual quality. MetaSapiens combines three techniques. First, we present an efficiency-aware pruning technique to optimize rendering speed. Second, we introduce a Foveated Rendering (FR) method for PBNR, leveraging humans' low visual acuity in peripheral regions to relax rendering quality and improve rendering speed. Finally, we propose an accelerator design for FR, addressing the load imbalance issue in (FR-based) PBNR. Our evaluation shows that our system achieves an order of magnitude speedup over existing PBNR models without sacrificing subjective visual quality, as confirmed by a user study. The code and demo are available at: https://horizon-lab.org/metasapiens/.

cs.GR↗

arXiv2024

VAP: The Vulnerability-Adaptive Protection Paradigm Toward Reliable Autonomous Machines

The next ubiquitous computing platform, following personal computers and smartphones, is poised to be inherently autonomous, encompassing technologies like drones, robots, and self-driving cars. Ensuring reliability for these autonomous machines is critical. However, current resiliency solutions make fundamental trade-offs between reliability and cost, resulting in significant overhead in performance, energy consumption, and chip area. This is due to the "one-size-fits-all" approach commonly used, where the same protection scheme is applied throughout the entire software computing stack. This paper presents the key insight that to achieve high protection coverage with minimal cost, we must leverage the inherent variations in robustness across different layers of the autonomous machine software stack. Specifically, we demonstrate that various nodes in this complex stack exhibit different levels of robustness against hardware faults. Our findings reveal that the front-end of an autonomous machine's software stack tends to be more robust, whereas the back-end is generally more vulnerable. Building on these inherent robustness differences, we propose a Vulnerability-Adaptive Protection (VAP) design paradigm. In this paradigm, the allocation of protection resources - whether spatially (e.g., through modular redundancy) or temporally (e.g., via re-execution) - is made inversely proportional to the inherent robustness of tasks or algorithms within the autonomous machine system. Experimental results show that VAP provides high protection coverage while maintaining low overhead in both autonomous vehicle and drone systems.

cs.RO↗

arXiv2024

Computational Trichromacy Reconstruction: Empowering the Color-Vision Deficient to Recognize Colors Using Augmented Reality

We propose an assistive technology that helps individuals with Color Vision Deficiencies (CVD) to recognize/name colors. A dichromat's color perception is a reduced two-dimensional (2D) subset of a normal trichromat's three dimensional color (3D) perception, leading to confusion when visual stimuli that appear identical to the dichromat are referred to by different color names. Using our proposed system, CVD individuals can interactively induce distinct perceptual changes to originally confusing colors via a computational color space transformation. By combining their original 2D precepts for colors with the discriminative changes, a three dimensional color space is reconstructed, where the dichromat can learn to resolve color name confusions and accurately recognize colors. Our system is implemented as an Augmented Reality (AR) interface on smartphones, where users interactively control the rotation through swipe gestures and observe the induced color shifts in the camera view or in a displayed image. Through psychophysical experiments and a longitudinal user study, we demonstrate that such rotational color shifts have discriminative power (initially confusing colors become distinct under rotation) and exhibit structured perceptual shifts dichromats can learn with modest training. The AR App is also evaluated in two real-world scenarios (building with lego blocks and interpreting artistic works); users all report positive experience in using the App to recognize object colors that they otherwise could not.

cs.HC↗

arXiv2024

An extremely active repeating fast radio burst source in a likely non-magneto-ionic environment

Fast radio bursts (FRBs) are bright radio bursts originating at cosmological distances. Only three repeating FRBs FRB 20121102A, FRB 20190520B and FRB 20201124A among $\sim$ 60 known repeating FRBs have circular polarization. We observed the FRB 20220912A with the Robert C. Byrd Green Bank Telescope (GBT) at L-band on 24 October 2022 and detected 128 bursts in 1.4 hours, corresponding to a burst rate of about 90 hr$^{-1}$, which is the highest yet for FRBs observed by the GBT. The average rotation measure (RM) was $-$0.4$\pm$0.3$\,$rad$\,$m$^{-2}$ with negligible intraday RM change, indicating a likely non-magneto-ionic environment. 61% bursts have linear polarization fraction greater than 90%. Approximately 56% of the bright bursts have circular polarization. A downward drift in frequency and polarization angle swings were found in our sample. The characterization of FRB 20220912A indicates that the circular polarization is unlikely to be caused by the magneto-ionic environment for at least some of the repeating FRB population.

astro-ph.HE↗

arXiv2024

CoolerSpace: A Language for Physically Correct and Computationally Efficient Color Programming

Color programmers manipulate lights, materials, and the resulting colors from light-material interactions. Existing libraries for color programming provide only a thin layer of abstraction around matrix operations. Color programs are, thus, vulnerable to bugs arising from mathematically permissible but physically meaningless matrix computations. Correct implementations are difficult to write and optimize. We introduce CoolerSpace to facilitate physically correct and computationally efficient color programming. CoolerSpace raises the level of abstraction of color programming by allowing programmers to focus on describing the logic of color physics. Correctness and efficiency are handled by CoolerSpace. The type system in CoolerSpace assigns physical meaning and dimensions to user-defined objects. The typing rules permit only legal computations informed by color physics and perception. Along with type checking, CoolerSpace also generates performance-optimized programs using equality saturation. CoolerSpace is implemented as a Python library and compiles to ONNX, a common intermediate representation for tensor computations. CoolerSpace not only prevents common errors in color programming, but also does so without run-time overhead: even unoptimized CoolerSpace programs out-perform existing Python-based color programming systems by up to 5.7 times; our optimizations provide up to an additional 1.4 times speed-up.

cs.PL↗

arXiv2024

Potamoi: Accelerating Neural Rendering via a Unified Streaming Architecture

Neural Radiance Field (NeRF) has emerged as a promising alternative for photorealistic rendering. Despite recent algorithmic advancements, achieving real-time performance on today's resource-constrained devices remains challenging. In this paper, we identify the primary bottlenecks in current NeRF algorithms and introduce a unified algorithm-architecture co-design, Potamoi, designed to accommodate various NeRF algorithms. Specifically, we introduce a runtime system featuring a plug-and-play algorithm, SpaRW, which significantly reduces the per-frame computational workload and alleviates compute inefficiencies. Furthermore, our unified streaming pipeline coupled with customized hardware support effectively tames both SRAM and DRAM inefficiencies by minimizing repetitive DRAM access and completely eliminating SRAM bank conflicts. When evaluated against a baseline utilizing a dedicated DNN accelerator, our framework demonstrates a speed-up and energy reduction of 53.1$\times$ and 67.7$\times$, respectively, all while maintaining high visual quality with less than a 1.0 dB reduction in peak signal-to-noise ratio.

cs.AR↗

arXiv2024

Artificial Immune System of Secure Face Recognition Against Adversarial Attacks

Insect production for food and feed presents a promising supplement to ensure food safety and address the adverse impacts of agriculture on climate and environment in the future. However, optimisation is required for insect production to realise its full potential. This can be by targeted improvement of traits of interest through selective breeding, an approach which has so far been underexplored and underutilised in insect farming. Here we present a comprehensive review of the selective breeding framework in the context of insect production. We systematically evaluate adjustments of selective breeding techniques to the realm of insects and highlight the essential components integral to the breeding process. The discussion covers every step of a conventional breeding scheme, such as formulation of breeding objectives, phenotyping, estimation of genetic parameters and breeding values, selection of appropriate breeding strategies, and mitigation of issues associated with genetic diversity depletion and inbreeding. This review combines knowledge from diverse disciplines, bridging the gap between animal breeding, quantitative genetics, evolutionary biology, and entomology, offering an integrated view of the insect breeding research area and uniting knowledge which has previously remained scattered across diverse fields of expertise.

cs.CV↗

arXiv2024

Scintillation velocity and arc observations of FRB 20201124A

We present the scintillation velocity measurements of FRB~20201124A from the FAST observations, which reveal an annual variation. This annual variation is further supported by changes detected in the scintillation arc as observed from the secondary spectrum. We attribute the annual velocity variation to the presence of a moderately anisotropic scattering screen located at a distance of 0.4$\pm$0.1~kpc from Earth. Our results prove that the scintillation of this FRB is mainly caused by material close to Earth on a Galactic scale. However, scintillation observations of other FRBs may expose their surrounding environment or uncover possible orbital motion if scintillation is caused by materials in their host galaxy.

astro-ph.HE↗

arXiv2024

BlissCam: Boosting Eye Tracking Efficiency with Learned In-Sensor Sparse Sampling

Eye tracking is becoming an increasingly important task domain in emerging computing platforms such as Augmented/Virtual Reality (AR/VR). Today's eye tracking system suffers from long end-to-end tracking latency and can easily eat up half of the power budget of a mobile VR device. Most existing optimization efforts exclusively focus on the computation pipeline by optimizing the algorithm and/or designing dedicated accelerators while largely ignoring the front-end of any eye tracking pipeline: the image sensor. This paper makes a case for co-designing the imaging system with the computing system. In particular, we propose the notion of "in-sensor sparse sampling", whereby the pixels are drastically downsampled (by 20x) within the sensor. Such in-sensor sampling enhances the overall tracking efficiency by significantly reducing 1) the power consumption of the sensor readout chain and sensor-host communication interfaces, two major power contributors, and 2) the work done on the host, which receives and operates on far fewer pixels. With careful reuse of existing pixel circuitry, our proposed BLISSCAM requires little hardware augmentation to support the in-sensor operations. Our synthesis results show up to 8.2x energy reduction and 1.4x latency reduction over existing eye tracking pipelines.

cs.AR↗

arXiv2024

Cicero: Addressing Algorithmic and Architectural Bottlenecks in Neural Rendering by Radiance Warping and Memory Optimizations

Neural Radiance Field (NeRF) is widely seen as an alternative to traditional physically-based rendering. However, NeRF has not yet seen its adoption in resource-limited mobile systems such as Virtual and Augmented Reality (VR/AR), because it is simply extremely slow. On a mobile Volta GPU, even the state-of-the-art NeRF models generally execute only at 0.8 FPS. We show that the main performance bottlenecks are both algorithmic and architectural. We introduce, CICERO, to tame both forms of inefficiencies. We first introduce two algorithms, one fundamentally reduces the amount of work any NeRF model has to execute, and the other eliminates irregular DRAM accesses. We then describe an on-chip data layout strategy that eliminates SRAM bank conflicts. A pure software implementation of CICERO offers an 8.0x speed-up and 7.9x energy saving over a mobile Volta GPU. When compared to a baseline with a dedicated DNN accelerator, our speed-up and energy reduction increase to 28.2x and 37.8x, respectively - all with minimal quality loss (less than 1.0 dB peak signal-to-noise ratio reduction).

cs.AR↗

arXiv2024

Characterizing Soft-Error Resiliency in Arm's Ethos-U55 Embedded Machine Learning Accelerator

As Neural Processing Units (NPU) or accelerators are increasingly deployed in a variety of applications including safety critical applications such as autonomous vehicle, and medical imaging, it is critical to understand the fault-tolerance nature of the NPUs. We present a reliability study of Arm's Ethos-U55, an important industrial-scale NPU being utilised in embedded and IoT applications. We perform large scale RTL-level fault injections to characterize Ethos-U55 against the Automotive Safety Integrity Level D (ASIL-D) resiliency standard commonly used for safety-critical applications such as autonomous vehicles. We show that, under soft errors, all four configurations of the NPU fall short of the required level of resiliency for a variety of neural networks running on the NPU. We show that it is possible to meet the ASIL-D level resiliency without resorting to conventional strategies like Dual Core Lock Step (DCLS) that has an area overhead of 100%. We achieve so through selective protection, where hardware structures are selectively protected (e.g., duplicated, hardened) based on their sensitivity to soft errors and their silicon areas. To identify the optimal configuration that minimizes the area overhead while meeting the ASIL-D standard, the main challenge is the large search space associated with the time-consuming RTL simulation. To address this challenge, we present a statistical analysis tool that is validated against Arm silicon and that allows us to quickly navigate hundreds of billions of fault sites without exhaustive RTL fault injections. We show that by carefully duplicating a small fraction of the functional blocks and hardening the Flops in other blocks meets the ASIL-D safety standard while introducing an area overhead of only 38%.

cs.AR↗