arXiv subjects

Cheng Liu

Publications and source records attributed to Cheng Liu.

At least 109 records · Page 6Linked to original sources

arXiv2024

From photon momentum transfer to acceleration sensing

As a typical application of photon momentum transfer, optical levitation systems are known for their ideal isolation from mechanical dissipation and thermal noise. These characters offer extraordinary potential for acceleration precision sensing and have attracted extensive attention in both fundamental and applied physics. Although considerable improvements of optical levitation accelerometers has been reported, the dynamic testing of the sensing performance remains a crucial challenge before the utilization in practical application scenarios. In this work, we present a dual-beam optical levitation accelerometer and demonstrate the test with dynamic inputs for the first time. An acceleration sensing sensitivity of $0.1μg$ and a measurement range of $ 1g$ are achieved. These advancements solidify the potential of optical levitation accelerometer for deployment in practical domains, including navigation, intelligent driving, and industrial automation, building a bridge between the laboratory systems and real-world applications.

physics.optics↗

arXiv2024

HLSPilot: LLM-based High-Level Synthesis

Large language models (LLMs) have catalyzed an upsurge in automatic code generation, garnering significant attention for register transfer level (RTL) code generation. Despite the potential of RTL code generation with natural language, it remains error-prone and limited to relatively small modules because of the substantial semantic gap between natural language expressions and hardware design intent. In response to the limitations, we propose a methodology that reduces the semantic gaps by utilizing C/C++ for generating hardware designs via High-Level Synthesis (HLS) tools. Basically, we build a set of C-to-HLS optimization strategies catering to various code patterns, such as nested loops and local arrays. Then, we apply these strategies to sequential C/C++ code through in-context learning, which provides the LLMs with exemplary C/C++ to HLS prompts. With this approach, HLS designs can be generated effectively. Since LLMs still face problems in determining the optimized pragma parameters precisely, we have a design space exploration (DSE) tool integrated for pragma parameter tuning. Furthermore, we also employ profiling tools to pinpoint the performance bottlenecks within a program and selectively convert bottleneck components to HLS code for hardware acceleration. By combining the LLM-based profiling, C/C++ to HLS translation, and DSE, we have established HLSPilot, the first LLM-enabled high-level synthesis framework, which can fully automate the high-level application acceleration on hybrid CPU-FPGA architectures. According to our experiments on real-world application benchmarks, HLSPilot achieve comparable performance in general and can even outperform manually crafted counterparts, thereby underscoring the substantial promise of LLM-assisted hardware designs.

cs.AR↗

arXiv2024

Crack-free high composition (>35%) thick (>30 nm) barrier AlGaN/AlN/GaN HEMT on sapphire with record low sheet resistance

In this article, high composition (>35%) thick (>30 nm) barrier AlGaN/AlN/GaN HEMT structure grown on a sapphire substrate with ultra-low sheet resistivity (<250 Ω/ \Box ) is reported. Optimization of growth conditions, such as reduced growth rate, low carbon incorporation, and thickness optimization of different epitaxial layers allowed to grow a crack-free high composition and thick AlGaN barrier layer HEMT structure. A significantly high two-dimensional electron gas (2DEG) density of 1.46 \times 10^{13} cm^{-2} with a room temperature mobility of 1710 cm^{2}/V.s is obtained by Hall measurement using the Van-Der-Pauw method. These state-of-the-art results show great potential for high-power Ga-polar HEMT design on the sapphire substrate.

physics.app-ph↗

arXiv2024

SigDLA: A Deep Learning Accelerator Extension for Signal Processing

Deep learning and signal processing are closely correlated in many IoT scenarios such as anomaly detection to empower intelligence of things. Many IoT processors utilize digital signal processors (DSPs) for signal processing and build deep learning frameworks on this basis. While deep learning is usually much more computing-intensive than signal processing, the computing efficiency of deep learning on DSPs is limited due to the lack of native hardware support. In this case, we present a contrary strategy and propose to enable signal processing on top of a classical deep learning accelerator (DLA). With the observation that irregular data patterns such as butterfly operations in FFT are the major barrier that hinders the deployment of signal processing on DLAs, we propose a programmable data shuffling fabric and have it inserted between the input buffer and computing array of DLAs such that the irregular data is reorganized and the processing is converted to be regular. With the online data shuffling, the proposed architecture, SigDLA, can adapt to various signal processing tasks without affecting the deep learning processing. Moreover, we build a reconfigurable computing array to suit the various data width requirements of both signal processing and deep learning. According to our experiments, SigDLA achieves an average performance speedup of 4.4$\times$, 1.4$\times$, and 1.52$\times$, and average energy reduction of 4.82$\times$, 3.27$\times$, and 2.15$\times$ compared to an embedded ARM processor with customized DSP instructions, a DSP processor, and an independent DSP-DLA architecture respectively with 17% more chip area over the original DLAs.

cs.AR↗

arXiv2024

Graphitron: A Domain Specific Language for FPGA-based Graph Processing Accelerator Generation

FPGA-based graph processing accelerators, enabling extensive customization, have demonstrated significant energy efficiency over general computing engines like CPUs and GPUs. Nonetheless, customizing accelerators to diverse graph processing algorithms with distinct computational patterns remains challenging and error-prone for high-level application users. To this end, template-based approaches have been developed to automate the graph processing accelerator generation. Although these frameworks significantly enhance the design productivity, the templates often result in closely coupled algorithms, programming models, and architectures, severely limiting the versatility of the targeted graph processing algorithms and their applicability to high-level users. Furthermore, the limitations of the frameworks are usually ambiguous due to the absence of a rigorous grammar definition. To overcome these challenges, we introduce Graphitron, a domain-specific language (DSL), which allows users to generate customized accelerators for a wide range of graph processing algorithms on FPGAs without engaging with the complexities of low-level FPGA designs. Graphitron, by defining vertices and edges as primitive data types, naturally facilitates the description of graph algorithms using edge-centric or vertex-centric programming models. The Graphitron back-end employs a suite of hardware optimization techniques including pipelining, data shuffling, and memory access optimization that are independent with the specific algorithms, supporting the creation of versatile graph processing accelerators. Our experiments indicate that accelerators crafted using Graphitron achieve comparable performance to that generated with template-based design framework. Moreover, it exhibits exceptional flexibility in algorithm expression and significantly enhance accelerator design productivity.

cs.AR↗

arXiv2024

MCU-MixQ: A HW/SW Co-optimized Mixed-precision Neural Network Design Framework for MCUs

Mixed-precision neural network (MPNN) that utilizes just enough data width for the neural network processing is an effective approach to meet the stringent resources constraints including memory and computing of MCUs. Nevertheless, there is still a lack of sub-byte and mixed-precision SIMD operations in MCU-class ISA and the limited computing capability of MCUs remains underutilized, which further aggravates the computing bound encountered in neural network processing. As a result, the benefits of MPNNs cannot be fully unleashed. In this work, we propose to pack multiple low-bitwidth arithmetic operations within a single instruction multiple data (SIMD) instructions in typical MCUs, and then develop an efficient convolution operator by exploring both the data parallelism and computing parallelism in convolution along with the proposed SIMD packing. Finally, we further leverage Neural Architecture Search (NAS) to build a HW/SW co-designed MPNN design framework, namely MCU-MixQ. This framework can optimize both the MPNN quantization and MPNN implementation efficiency, striking an optimized balance between neural network performance and accuracy. According to our experiment results, MCU-MixQ achieves 2.1$\times$ and 1.4$\times$ speedup over CMix-NN and MCUNet respectively under the same resource constraints.

cs.AR↗

arXiv2024

ApproxPilot: A GNN-based Accelerator Approximation Framework

A typical optimization of customized accelerators for error-tolerant applications such as multimedia, recognition, and classification is to replace traditional arithmetic units like multipliers and adders with the approximate ones to enhance energy efficiency while adhering to accuracy requirements. However, the plethora of arithmetic units and diverse approximate unit options result in an exceedingly large design space. Therefore, there is a pressing need for an end-to-end design framework capable of navigating this intricate design space for approximation optimization. Traditional methods relying on simulation-based or blackbox model evaluations suffer from either high computational costs or limitations in accuracy and scalability, posing significant challenges to the optimization process. In this paper, we propose a Graph Neural Network (GNN) model that leverages the physical connections of arithmetic units to capture their influence on the performance, power, area (PPA), and accuracy of the accelerator. Particularly, we notice that critical path plays a key role in node feature of the GNN model and having it embedded in the feature vector greatly enhances the prediction quality of the models. On top of the models that allow rapid and efficient PPA and accuracy prediction of various approximate accelerator configurations, we can further explore the large design space effectively and build an end-to-end accelerator approximation framework named ApproxPilot to optimize the accelerator approximation. Our experimental results demonstrate that ApproxPilot outperforms state-of-the-art approximation optimization frameworks in both performance and hardware overhead with the same accuracy constraints.

cs.AR↗

arXiv2024

Identifying while Learning for Document Event Causality Identification

Event Causality Identification (ECI) aims to detect whether there exists a causal relation between two events in a document. Existing studies adopt a kind of identifying after learning paradigm, where events' representations are first learned and then used for the identification. Furthermore, they mainly focus on the causality existence, but ignoring causal direction. In this paper, we take care of the causal direction and propose a new identifying while learning mode for the ECI task. We argue that a few causal relations can be easily identified with high confidence, and the directionality and structure of these identified causalities can be utilized to update events' representations for boosting next round of causality identification. To this end, this paper designs an *iterative learning and identifying framework*: In each iteration, we construct an event causality graph, on which events' causal structure representations are updated for boosting causal identification. Experiments on two public datasets show that our approach outperforms the state-of-the-art algorithms in both evaluations for causality existence identification and direction identification.

cs.CL↗

arXiv2024

Simultaneous ground-state cooling of two levitated nanoparticles by coherent scattering

Simultaneous ground-state cooling of two levitated nanoparticles is a crucial prerequisite for investigation of macroscopic quantum effects such as quantum entanglement and quantum correlation involving translational motion of particles. Here we consider a coupled cavity-levitated-particle system and present a detailed derivation of its Hamiltonian. We find that the $y$-direction motions of the two particles are decoupled from the cavity field and both the $x$- and $z$-direction motions, and that the $z$-direction motions can be further decoupled from the cavity field and the $x$-direction motions by choosing proper locations of the particles. We study the simultaneous cooling of these mechanical modes in both the three-mode and five-mode cavity-levitated optomechanical models. It is found that there exists the dark-mode effect when the two tweezers have the same powers, which suppress the simultaneous ground-state cooling. Nevertheless, the simultaneous ground-state cooling of these modes can be realized by breaking the dark-mode effect under proper parameters. Our system provides a versatile platform to study quantum effects and applications in cavity-levitated optomechanical systems.

quant-ph↗

arXiv2024

The first low-mass eclipsing binary within the fully convective zone from TMTS

We present a comprehensive photometric and spectroscopic analysis of the short-period ($\sim$5.32 hours) and low-mass eclipsing binary TMTSJ0803 discovered by Tsinghua-Ma Huateng Telescope for Survey (TMTS). By fitting the light curves and radial velocity data with the Wilson--Devinney code, we find that the binary is composed of two late spotted active M dwarfs below the fully convective boundary. This is supported by the discovery of a significant Balmer emission lines in the LAMOST spectrum and prominent coronal X-ray emission. In comparison with the typical luminosity of rapidly rotating fully convective stars, the much brighter X-ray luminosity ($L_{X}/L_{\rm{bol}} = 0.0159 \pm 0.0059$) suggests the stellar magnetic activity of fully convective stars could be enhanced in such a close binary system. Given the metallicity of [M/H] = $-$ 0.35 dex as inferred from the LAMOST spectrum, we measure the masses and radii of both stars to be $M_{1} = 0.169 \pm 0.010~M_{\odot}$, $M_{2} = 0.162 \pm 0.016~M_{\odot}$, $R_{1} = 0.170 \pm 0.006~R_{\odot}$, and $R_{2} = 0.156 \pm 0.006~R_{\odot}$, respectively. Based on the luminosity ratio from the light curve modeling, the effective temperatures of two components are also estimated. In comparison with the stellar evolution models, the radii and effective temperatures of two components are all below the isochrones. The radius deflation might be mainly biased by a small radial velocity (RV) data or (and) a simple correction on RVs, while the discrepancy in effective temperature might be due to the enhanced magnetic activity in this binary.

astro-ph.SR↗

arXiv2024

Enhanced Blandford Znajek Jet in Loop Quantum Black Hole

The Blandford-Znajek (BZ) process powers energetic jets by extracting the rotating energy of a Kerr black hole. It is important to understand this process in non-Kerr black hole spacetimes. In this study, we conduct two-dimensional and three-dimensional two-temperature General Relativistic Magnetohydrodynamic (GRMHD) simulations of magnetized accretion flows onto a rotating Loop-Quantum black hole (LQBH). Our investigation focuses on the accretion flow structure and jet launching dynamics from our simulations. We observe that the loop quantum effects increase the black hole angular frequency for spinning black holes.This phenomenon intensifies the frame-dragging effect, leading to an amplification of the toroidal magnetic field within the funnel region and enhancement of the launching jet power. It is possible to fit the jet power following a similar fitting formula of the black hole angular frequency as seen in the Kerr black hole. Based on the General Relativistic Radiation Transfer (GRRT) calculation, we find that the jet image from LQBH has a wider opening angle and an extended structure than the Kerr BH.

astro-ph.HE↗

arXiv2024

Soft Error Reliability Analysis of Vision Transformers

Vision Transformers (ViTs) that leverage self-attention mechanism have shown superior performance on many classical vision tasks compared to convolutional neural networks (CNNs) and gain increasing popularity recently. Existing ViTs works mainly optimize performance and accuracy, but ViTs reliability issues induced by soft errors in large-scale VLSI designs have generally been overlooked. In this work, we mainly study the reliability of ViTs and investigate the vulnerability from different architecture granularities ranging from models, layers, modules, and patches for the first time. The investigation reveals that ViTs with the self-attention mechanism are generally more resilient on linear computing including general matrix-matrix multiplication (GEMM) and full connection (FC) and show a relatively even vulnerability distribution across the patches. ViTs involve more fragile non-linear computing such as softmax and GELU compared to typical CNNs. With the above observations, we propose a lightweight block-wise algorithm-based fault tolerance (LB-ABFT) approach to protect the linear computing implemented with distinct sizes of GEMM and apply a range-based protection scheme to mitigate soft errors in non-linear computing. According to our experiments, the proposed fault-tolerant approaches enhance ViTs accuracy significantly with minor computing overhead in presence of various soft errors.

cs.CR↗

arXiv2024

Variable white dwarfs in TMTS: Asteroseismological analysis of a ZZ Ceti star, TMTS J17184064+2524314

The Tsinghua University-Ma Huateng Telescope for Survey (TMTS) has been constantly monitoring the northern sky since 2020 in search of rapidly variable stars. To find variable white dwarfs (WDs), the TMTS catalog is cross-matched with the WD catalog of Gaia EDR3, resulting in over 3000 light curves of WD candidates. The WD TMTS J17184064+2524314 (hereafter J1718) is the second ZZ~Ceti star discovered among these common sources. Based on the light curves from TMTS, follow-up photometric observations, and TESS, 10 periods and 3 combination periods are detected. A rotation period of $25.12\pm0.18$ hr is derived, according to the identified rotational splitting. Our spectroscopic observation indicates that this WD belongs to DA type with $T_{\rm eff}=11,670\pm604$ K, log $g=8.16\pm0.36$, $M = 0.70\pm0.23$ M$_{\odot}$, and age=$0.51\pm0.34$ Gyr. Based on core-parameterized asteroseismological model grids ($\geqslant$ 14 million), we derive a best-fit solution of $T_{\rm eff}=11,640\pm20$ K, log $g=8.267\pm0.008$, and $M = 0.750\pm0.005$ M$_{\odot}$ for J1718, consistent with the spectral fitting results. For this WD, the corresponding carbon and oxygen abundances in the core are 0.43 and 0.57, respectively. The distance derived from the intrinsic luminosity given by asteroseismology is $64\pm15$ pc, in accord with the distance of $70.1\pm0.2$ pc from Gaia DR3 within the uncertainties.

astro-ph.SR↗

arXiv2024

Prospects for Joint Detection of Gravitational Waves with Counterpart Gamma-Ray Bursts Detected by the HADAR Experiment

The detection of GW170817/GRB170817A implied the strong association between short gamma-ray bursts (SGRBs) and binary neutron star (BNS) mergers which produce gravitational waves (GWs). More evidence is needed to confirm the association and reveal the physical processes of BNS mergers. The upcoming High Altitude Detection of Astronomical Radiation (HADAR) experiment, excelling in a wide field of view (FOV) and a large effective area above tens of GeV, is a hope for the prompt detection of very-high-energy (VHE; > 10 GeV) SGRBs. The aim of this paper is to simulate and analyse GW/SGRB joint detections by future GW detector networks in synergy with HADAR, including the second generation LIGO, Virgo and KAGRA and the third generation ET and CE. We provide a brief introduction of the HADAR experiment for SGRB simulations and its expected SGRB detections. For GW simulations, we adopt a phenomenological model to describe GWs produced by BNS mergers and introduce the signal-noise ratios (SNRs) as detector responses. Following a theoretical analysis we compute the redshift-dependent efficiency functions of GW detector networks. We then construct the simulation of GW detection by Monte Carlo sampling. We compare the simulated results of LIGO-Virgo O2 and O3 runs with their actual detections as a check. The combination of GW and SGRB models is then discussed for joint detection, including parameter correlations, triggered SNRs and efficiency skymaps. The estimated joint detection rates are 0.09-2.52 per year for LHVK network with HADAR under different possible configurations, and approximately 0.27-7.89 per year for ET+CE network with HADAR.

astro-ph.HE↗

arXiv2024

Optically Levitated Nanoparticles as Receiving Antennas for Low Frequency Wireless Communication

Low-frequency (LF) wireless communications play a crucial role in ensuring anti-interference, long-range, and efficient communication across various environments. However, in conventional LF communication systems, their antenna size is required to be inversely proportional to the wavelength, so that their mobility and flexibility are greatly limited. Here we introduce a novel prototype of LF receiving antennas based on optically levitated nanoparticles, which overcomes the size-frequency limitation to reduce the antenna size to the hundred-nanometer scale. These charged particles are extremely sensitive to external electric field as mechanical resonators, and their resonant frequencies are adjustable. The effectiveness of these antennas was experimentally demonstrated by using the frequency shift keying (2FSK) modulation scheme. The experimental results indicate a correlation between error rate and factors such as transmission rate, signal strength, and vacuum degree with a signal strength of approximately 0.1V/m and a bit error rate below 0.1%. This advancement in leveraging levitated particle mechanical resonators (LPMRs) as LF antennas marks a significant stride in long-distance communication technology.

physics.app-ph↗

arXiv2023

Shadows of Loop Quantum Black Holes: Semi-analytical Simulations of Loop Quantum Gravity Effects on Sagittarius A* and M 87*

In this study, we delve into the observational implications of rotating Loop Quantum Black Holes (LQBHs) within an astrophysical framework. We employ semi-analytical General Relativistic Radiative Transfer (GRRT) computations to study the emission from the accretion flow around LQBHs. Our findings indicate that the increase of Loop Quantum Gravity (LQG) effects results in an enlargement of the rings from LQBHs, thereby causing a more circular polarization pattern in the shadow images. We make comparisons with the Event Horizon Telescope (EHT) observations of Sgr\,A$^*$ and M\,87$^*$, which enable us to determine an upper limit for the polymetric function $P$ in LQG. The upper limit for Sgr\,A$^*$ is $0.2$, while for M\,87$^*$ it is $0.07$. Both black holes exhibit a preference for a relatively high spin ($a\gtrsim0.5$ for Sgr\,A$^*$ and $0.5\lesssim a \lesssim 0.7$ for M\,87$^*$). The constraints for Sgr\,A$^*$ are based on black hole spin and ring diameter, whereas for M\,87$^*$, the constraints are further tightened by the polarimetric pattern. In essence, our simulations provide observational constraints on the effect of LQG in supermassive black holes (SMBH), providing the most consistent comparison with observation.

gr-qc↗

arXiv2023

MRFI: An Open Source Multi-Resolution Fault Injection Framework for Neural Network Processing

To ensure resilient neural network processing on even unreliable hardware, comprehensive reliability analysis against various hardware faults is generally required before the deep neural network models are deployed, and efficient error injection tools are highly demanded. However, most existing fault injection tools remain rather limited to basic fault injection to neurons and fail to provide fine-grained vulnerability analysis capability. In addition, many of the fault injection tools still need to change the neural network models and make the fault injection closely coupled with normal neural network processing, which further complicates the use of the fault injection tools and slows down the fault simulation. In this work, we propose MRFI, a highly configurable multi-resolution fault injection tool for deep neural networks. It enables users to modify an independent fault configuration file rather than neural network models for the fault injection and vulnerability analysis. Particularly, it integrates extensive fault analysis functionalities from different perspectives and enables multi-resolution investigation of the vulnerability of neural networks. In addition, it does not modify the major neural network computing framework of PyTorch. Hence, it allows parallel processing on GPUs naturally and exhibits fast fault simulation according to our experiments.

cs.LG↗

arXiv2023

Cross-Layer Optimization for Fault-Tolerant Deep Learning

Fault-tolerant deep learning accelerator is the basis for highly reliable deep learning processing and critical to deploy deep learning in safety-critical applications such as avionics and robotics. Since deep learning is known to be computing- and memory-intensive, traditional fault-tolerant approaches based on redundant computing will incur substantial overhead including power consumption and chip area. To this end, we propose to characterize deep learning vulnerability difference across both neurons and bits of each neuron, and leverage the vulnerability difference to enable selective protection of the deep learning processing components from the perspective of architecture layer and circuit layer respectively. At the same time, we observe the correlation between model quantization and bit protection overhead of the underlying processing elements of deep learning accelerators, and propose to reduce the bit protection overhead by adding additional quantization constrain without compromising the model accuracy. Finally, we employ Bayesian optimization strategy to co-optimize the correlated cross-layer design parameters at algorithm layer, architecture layer, and circuit layer to minimize the hardware resource consumption while fulfilling multiple user constraints including reliability, accuracy, and performance of the deep learning processing at the same time.

cs.AR↗