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IEEE Transactions on Signal and Power Integrity 2024年 3卷 37-46页

作者： Chen, Quankun Ma, Hanzhi Li, Da Tao, Tuomin Tan, Shurun Liu, En-Xiao Schutt-Aine, Jose Li, Er-Ping Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems and Applications College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University University of Illinois at Urbana-Champaign Institute Haining314400 China A∗star Institute of High Performance Computing Singapore138632 Singapore University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL61820 United States

This article introduces a comprehensive approach for designing and analyzing signal integrity in heterogeneous integrated systems that incorporate neuromorphic Darwin chips. The proposed integrated system architecture includes a neuromorphic Darwin chip, digital signal processing unit, microcontroller unit, field programmable gate arrays, and coding and decoding modules to encode and reconstruct analog spiking signals. The study evaluates the encoding module and the heterogeneous integration structure and conducts signal integrity analysis. To achieve optimal signal integrity performance, the article proposes a novel binocular eye diagram analysis technique. This innovative approach guides the encoding algorithm modification and improves the overall system performance. This research is the first to combine joint field-circuit simulation, heterogeneous integration modeling, and signal integrity analysis of the Darwin neuromorphic chip, and it is expected to serve as a valuable reference for future studies on similar systems. © 2022 IEEE.

关键词： Neural networks

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A Cross-domain Radar Emitter Recognition Method with Few-shot Learning

A Cross-domain Radar Emitter Recognition Method with Few-sho...

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Computer Engineering and Intelligent Communications (ISCEIC), International Symposium on

作者： Yixian Luo Tianrui Liu Xueqiong Li Hengzhu Liu Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

In many realistic scenarios, it is necessary but challenging to acquire a large number of annotated radar emitter samples for training a recognition model. This study proposes a cross-domain radar emitter recognition method with few-shot learning, which introduces model-agnostic meta-learning (MAML) to recognize radar emitter and improves it to adapt to various types of radar emitter in different domains without spending a lot of time and data to retrain the model. This method can learn an ideal initialization parameter with a few source domain samples, and then initialize with this parameter on a completely different target domain. It can obtain good generalization effect by fine-tuning with fewer samples, so as to realize cross-domain recognition of radar emitter. Simulation results show that the accuracy of the proposed method can reach more than 90% in high-noise target domains with completely different data distribution from the source domain, which shows its superiority in recognition performance and sample size compared with other radar emitter recognition methods.

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通过表面终止化学设计实现高度可逆的锌金属阳极

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Science Bulletin 2023年第23期68卷 2993-3002,M0005页

作者：刘宇陈树麟袁豪熊方宇刘琴安永康张建勇吴璐孙建国 Yong-Wei Zhang 安琴友 John Wang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China Department of Materials Science and Engineering National University of SingaporeSingapore 117574Singapore Institute of High Performance Computing Agency for ScienceTechnology and Research(A*Star)Singapore 138632Singapore Institute of Materials Research and Engineering(IMRE) Agency for ScienceTechnology and Research(A*Star)Singapore 138634Singapore

锌表面自然形成的非均匀氧化层(ZnO)会与电解液发生反应形成Zn_(4)(OH)_(6)SO_(4)·xH_(2)O(ZSH)副产物,导致锌负极表面非均匀沉积,从而阻碍水性锌离子电池实际应用.为了排除氧化层分布形式的影响,本文利用表面终止化学设计策略在... 详细信息

锌表面自然形成的非均匀氧化层(ZnO)会与电解液发生反应形成Zn_(4)(OH)_(6)SO_(4)·xH_(2)O(ZSH)副产物,导致锌负极表面非均匀沉积,从而阻碍水性锌离子电池实际应用.为了排除氧化层分布形式的影响,本文利用表面终止化学设计策略在锌负极表面制备了一层完全氧化的ZnO层,研究发现锌负极均匀分布的ZnO层会原位演化形成致密的ZSH功能层.均匀且致密的ZSH能够有效隔绝电解液的腐蚀,从而有效提高锌负极界面的稳定性.此外,ZSH(002)晶面边缘的不饱和悬空键(如-O)更容易和Zn(H_(2)O)_(6)^(2+)结构中的锌离子形成强Zn-O键,可以促进Zn(H_(2)O)_(6)^(2+)的脱溶剂化和锌离子沿边扩散,实现了高度可逆的锌沉积行为并限制了副反应.基于此,本文提出与“表面脱水-晶体通道转移”机制明显不同的“边缘脱水-沿边缘转移”锌离子输运新机制.更重要的是,基于此组装的全电池显示出高循环稳定性(超过1000次循环)和高库仑效率(99.07%),验证了其潜在的实际应用价值.

关键词： Zn-O termination Zn Anode Solid electrolyte interphase Dendrite-free Zn ion battery

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MRDRL-ROS: A Multi Robot Deep Reinforcement Learning Platform based on Robot Operating System 4

MRDRL-ROS: A Multi Robot Deep Reinforcement Learning Platfor...

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2021 4th International Conference on Mechatronics and Computer technology Engineering, MCTE 2021

作者： Cai, Zhongxuan Liang, Zhen Ren, Jing Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Hunan Changsha410073 China

Deep reinforcement learning (DRL) has greatly improved the intelligence of AI in recent years and the community has proposed several common software to facilitate the development of DRL. However, in robotics the utility of common DRL software is limited and the development is time-consuming due to the complexity of various robot software. In this paper, we propose a software engineering approach leveraging modularity to facilitate robot DRL development. The platform decouples learning environment into task, simulator and hierarchical robot modules, which in turn enables diverse environment generation using existing modules as building blocks, regardless of the underlying robot software details. Experimental results show that our platform provides composable environment building, introduces high module reuse and efficiently facilitates robot DRL. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Reinforcement learning

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An improved reconfigurable logic in resistive random access memory

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Integration 2022年 87卷 169-175页

作者： Zhao, Yi Chen, Hui Liu, Peng Wu, Jigang Luo, Dongxiang The Center for High Performance Computing and Data Science School of Computer Science and Technology Guangdong University of Technology Guangdong510006 China School of Chemistry and Chemical Engineering/Institute of Clean Energy and Materials/Guangzhou Key Laboratory for Clean Energy and Materials/Huangpu Hydrogen Innovation Center Guangzhou University Guangzhou510006 China Institute of Semiconductors South China Normal University Guangzhou510631 China

Emerging non-volatile devices have shown great potential in computing in-memory (CIM). This work proposes a logic design method based on the complementary resistance switching (CRS) structure, which is connected by two anti-serially memristors. Three logics including AND, OR, and NIMP can be implemented in one step. Moreover, XOR logic can be implemented in two steps. Based on the proposed design, a 1-bit full adder is implemented with the fewer number of memristors and operation steps compared with existing works. The feasibility and correctness of our design are verified by the SPICE simulations with the Voltage Threshold Adaptive Memristor (VTEAM) model. In addition, we evaluate the ISCAS’85 benchmark circuit based on the proposed logic. Compared with existing methods, the performance and area overhead have been improved by the proposed method. © 2022 Elsevier B.V.

关键词： Memristors

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Automatic architecture design for distributed quantum computing

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Chinese Physics B 2024年第12期 62-77页

作者：骆挺宇郑宇真付祥邓玉欣 Shanghai Key Laboratory of Trustworthy Computing East China Normal University Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Tianjin Institute of Advanced Technology

In distributed quantum computing(DQC), quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip connections. Meanwhile, quantum software tries its best to reduce the required number of remote quantum gates between chips. However, this “hardware first, software follows” methodology may not fully exploit the potential of DQC. Inspired by classical software–hardware co-design, this paper explores the design space of application-specific DQC architectures. More specifically, we propose Auto Arch, an automated quantum chip network(QCN) structure design tool. With qubits grouping followed by a customized QCN design, AutoArch can generate a near-optimal DQC architecture suitable for target quantum algorithms. Experimental results show that the DQC architecture generated by Auto Arch can outperform other general QCN architectures when executing target quantum algorithms.

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Accelerating Blake3 in RISC-V

Accelerating Blake3 in RISC-V

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computing, Communication, Perception and Quantum technology (CCPQT), International Conference on

作者： Xiaofeng Zou Yuanxi Peng Tuo Li Lingjun Kong Zhaochun Pang Lu Zhang Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Second Research Institute Cloud Computing Equipment Innovation Center Jinan China

This paper proposes a novel hardware design to accelerate the Blake3 hash algorithm, a cryptographic hash function, on RISC-V architecture, using both vector extension (V1.0) and a specially designed Blake3 instruction. The design leverages the parallelism and simplicity of BLAKE3 to achieve high throughput and low latency, implementing the core compression function using a custom instruction set architecture that supports vector operations and constant computing. The Blake3 compression function is speed up in architecture based on hardware/software co-design. The design achieves a speed-up of 10 times for the compression function compare with a single core of 2.7GHz Xeon 8280 and 10% faster than an AVX512 implementation.

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Efficient SVV stabilized triangular spectral element methods for incompressible flows of high Reynolds numbers

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Advances in Aerodynamics 2022年第1期4卷 58-77页

作者： Lizhen Chen Tao Tang Chuanju Xu Beijing Computational Science Research Center Beijing100193People’s Republic of China Division of Science and Technology BNU-HKBU United International CollegeZhuhaiGuangdongPeople’s Republic of China Guangdong Provincial Key Laboratory of Computational Science and Material Design Southern University of Science and TechnologyShenzhenPeople’s Republic of China School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific ComputingXiamen UniversityXiamen361005People’s Republic of China

In this paper,we propose a spectral vanishing viscosity method for the triangular spectral element computation of high Reynolds number incompressible *** can be regarded as an extension of a similar stabilization technique for the standard spectral element *** difficulty of this extension lies in the fact that a suitable definition of spectral vanishing viscosity operator in non-structured elements does not exist,and it is not clear that if a suitably defined spectral vanishing viscosity provides desirable dissipation for the artificially accumulated *** main contribution of the paper includes:1)a well-defined spectral vanishing viscosity operator is proposed for non-standard spectral element methods for the Navier-Stokes equations based on triangular or tetrahedron partitions;2)an evaluation technique is introduced to efficiently implement the stabilization term without extra computational cost;3)the accuracy and efficiency of the proposed method is carefully examined through several numerical *** numerical results show that the proposed method not only preserves the exponential convergence,but also produces improved accuracy when applied to the unsteady Navier-Stokes equations having smooth ***,the stabilized triangular spectral element method efficiently stabilizes the simulation of high Reynolds incompressible flows.

关键词： Navier-Stokes equations Triangular spectral element method Spectral vanishing viscosity

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AutoGR: Automated geo-replication with fast system performance and preserved application semantics 47th

AutoGR: Automated geo-replication with fast system performan...

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47th International Conference on Very Large Data Bases, VLDB 2021

作者： Wang, Jiawei Li, Cheng Ma, Kai Huo, Jingze Yan, Feng Feng, Xinyu Xu, Yinlong University of Science and Technology of China China University of Nevada Reno United States State Key Laboratory for Novel Software Technology Nanjing University China Anhui Province Key Laboratory of High Performance Computing China

Geo-replication is essential for providing low latency response and quality Internet services. However, designing fast and correct geo-replicated services is challenging due to the complex trade-off between performance and consistency semantics in optimizing the expensive cross-site coordination. State-of-the-art solutions rely on programmers to derive sufficient application-specific invariants and code specifications, which is both time-consuming and errorprone. In this paper, we propose an end-to-end geo-replication deployment framework AutoGR (AUTOmated Geo-Replication) to free programmers from such label-intensive tasks. AutoGR enables the geo-replication features for non-replicated, serializable applications in an automated way with optimized performance and correct application semantics. Driven by a novel static analyzer Rigi, AutoGR can extract application invariants by verifying whether their geo-replicated versions obey the serializable semantics of the non-replicated application. Rigi takes application codes as inputs and infers a set of side effects and path conditions possibly leading to consistency violations. Rigi employs the Z3 theorem prover to identify pairs of conflicting side effects and feed them to a georeplication framework for automated across-site deployment. We evaluate AutoGR by transforming four serializable and originally non-replicated DB-compliant applications to geo-replicated ones across 3 sites. Compared with state-of-the-art human-interventionfree automated approaches (e.g., strong consistency), AutoGR reduces up to 61.8% latency and achieves up to 2.12× higher peak throughput. Compared with state-of-the-art approaches relying on a manual analysis (e.g., PoR), AutoGR can quickly enable the georeplication feature with zero human intervention while offering similarly low latency and high throughput. © by the owner/author(s).

关键词： Semantics

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A Segmentation-aware Synergy Network for Single Particle Recognition in Cryo-EM

A Segmentation-aware Synergy Network for Single Particle Rec...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Shuo Li, Hongjia Zhang, Chi Zhang, Fa Wan, Xiaohua Institute of Computing Technology Chinese Academy of Sciences High Performance Computer Research Center Beijing China University of Chinese Academy of Sciences Beijing China Zhejiang University State Key Laboratory of Cad&cg Hangzhou China Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665468190

Cryo-electron microscopy (cryo-EM) single particle analysis (SPA) has been an indispensable technology to reconstruct three-dimensional (3D) structures of biomolecules at near-atomic resolution. Tens of thousands of particles are required to obtain high-resolution 3D reconstructions, nevertheless, it is rather challenging due to the extremely noisy microscopy images and the diversity of particles. Recently, while deep learning-based methods have been devoted into the improvement of particle feature extraction and location estimation, most of them are plagued with vulnerable feature representation, inexact supervised ground truth. Furthermore, these DL-methods usually adopt denoising and particle picking as two-stage operations in the existing pipeline, which is inadequate to achieve accurate estimation for location. In this paper, we propose a segmentation-aware synergy framework to automatically select particles in which two tightly-coupled networks are designed including a multiple output convolution subnet for denoise to jointly learn strong object representation and pixel representation simultaneously and a deep convolution subnet for particle location. Furthermore, joint learning of the two networks can effectively enhance the synergy relationship between denoising and downstream recognition, thus leading to accurate and reliable location estimations for SPA. When applied with various EMPAIR real-world datasets, our model improves the performance of particle detection and exaction, especially intersection over union metric, and this strength has important implications for the next 2D alignment, 2D classification averaging, and high-resolution 3D refinement steps in SPA. © 2022 IEEE.

关键词： Convolution

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