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2022 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)最新文献

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Memristor-based Digital Circuits for Realizing the Pavlov's Associative Neural Network 实现巴甫洛夫联想神经网络的忆阻器数字电路
Yu Wang, Yi Liu, Jiayu Bao, Yu Yan, Ertao Hu, Xiang Wan, R. Xu, Haotong Zhang, Yi Tong
Memristors have sparked substantial interest in the hardware implementation of brain-inspired neuromorphic devices and systems. In this work, we propose a digital circuit to emulate the Pavlov's associative memory experiments based on fabricated Ag/TiO2/Pt memristors. Memristors operate as a logical signal processing unit in conjunction with the register to implement the emulation. The design of digital circuitry substantially increases the frequency of the system and reduces its power consumption and cost.
忆阻器引发了对大脑启发的神经形态设备和系统的硬件实现的极大兴趣。在这项工作中,我们提出了一个数字电路来模拟基于制备的Ag/TiO2/Pt记忆电阻器的巴甫洛夫联想记忆实验。忆阻器作为逻辑信号处理单元与寄存器一起工作以实现仿真。数字电路的设计大大提高了系统的频率,降低了系统的功耗和成本。
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引用次数: 1
Implementation of Polynomial Fitted Poly-Harmonic Distortion Model with Frequency Defined Device 频率定义器件多项式拟合多谐失真模型的实现
Xiaoqiang Tang, Jialin Cai
In this paper, a polynomial fitted poly-harmonic distortion (PHD) model is proposed, and it is implemented with frequency defined device (FDD). Polynomial fitting technique provides an effective method to including PHD model with different input power states through single set of model parameter. It can greatly reduce the model extraction complexity, and compact the model file size. The basic theory of PHD model, polynomial fitting method, and the FDD technique is provided in this work. A 10 W Gallium Nitride (GaN) packaged transistor is used in the test example. The results show that the proposed model has high accuracy for both fundamental and second harmonic behavioral predictions.
本文提出了一种多项式拟合的多谐失真(PHD)模型,并用频率自定义器件(FDD)实现该模型。多项式拟合技术通过单组模型参数,提供了包含不同输入功率状态的PHD模型的有效方法。它可以大大降低模型提取的复杂度,并压缩模型文件的大小。本文介绍了PHD模型的基本理论、多项式拟合方法和FDD技术。在测试实例中使用了10w的氮化镓封装晶体管。结果表明,该模型对基频和次谐波行为预测均具有较高的精度。
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引用次数: 1
A Fast Sampling Open-circuit Voltage Algorithm for Piezoelectric Energy Harvesting 压电能量采集的一种快速采样开路电压算法
Ying Yu, Xufeng Liao, Lianxi Liu
This paper presents a fast sampling open-circuit voltage algorithm for piezoelectric energy harvesting, which directly samples the output of the piezoelectric transducer and limits the sampling time to one piezoelectric vibration period. Therefore, it greatly reduces the power loss caused by the open-circuit sampling and improves the efficiency of the piezoelectric energy harvesting. The proposed circuit is implemented in 0.18 µm standard CMOS process, and the core circuit area is 670×536 µm2. The circuit can sample the open-circuit voltage in less than one piezoelectric vibration cycle and output the maximum power point voltage, and the power loss rate is less than 0.79%.
本文提出了一种用于压电能量采集的快速开路电压采样算法,该算法直接对压电换能器的输出进行采样,并将采样时间限制在一个压电振动周期内。因此,大大降低了开路采样造成的功率损耗,提高了压电能量采集的效率。该电路采用0.18µm标准CMOS工艺实现,核心电路面积为670×536µm2。该电路可以在不到一个压电振动周期内采样开路电压并输出最大功率点电压,功率损失率小于0.79%。
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引用次数: 0
Formation Mechanism of high Ni content (Cu, Ni)6Sn5 in Cu/Sn/Ni microbump for solid state aging Cu/Sn/Ni微碰撞固相时效中高Ni含量(Cu, Ni)6Sn5的形成机理
Haiyang Yu, C. Kao
Due to its low cost, the Cu/Sn/Ni microbump is the most widely used structure in electronic packaging. Recent studies have characterized the evolution of the microstructure and phase formation in this system, and a unique (Cu,Ni)6Sn5 phase has been discovered with a high Ni content. However, there has been debate over the formation mechanism of this phase. This study builds a model of the formation mechanism of (Cu,Ni)6Sn5 and provides direct proof.
Cu/Sn/Ni微凸点由于成本低,是电子封装中应用最广泛的结构。近年来的研究对该体系的微观结构演变和相形成进行了表征,发现了一种独特的(Cu,Ni)6Sn5相,具有较高的Ni含量。然而,对于这一相的形成机制一直存在争议。本研究建立了(Cu,Ni)6Sn5的形成机理模型,并提供了直接证据。
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引用次数: 0
TSV Defects Classification with Machine Learning Approaches 基于机器学习方法的TSV缺陷分类
Haitao He, Changhao Luo, Junchen Dong, Yudi Zhao, Min Miao, Kai Zhao
The S parameter amplitude, latency, resistance, and inductance of TSV-RDL structures with the presence of five kinds of defects are simulated as feature vectors for defect detection and classification. Three nondestructive defect classification schemes for the TSV-RDL structure in advanced packaging are evaluated. Feedforward neural network with rectified linear unit activation function for the backpropagation algorithm is superior for defect classification and may play an important role in design for test and build-in self-repair circuit design.
将存在5种缺陷的TSV-RDL结构的S参数幅值、时延、电阻和电感作为缺陷检测和分类的特征向量进行仿真。对先进封装中TSV-RDL结构的三种无损缺陷分类方案进行了评价。采用整流线性单元激活函数的前馈神经网络反向传播算法具有较好的缺陷分类能力,可在测试设计和内置自修复电路设计中发挥重要作用。
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引用次数: 0
ROPY-SLAM: a Heterogeneous CPU-FPGA System for Simultaneous Localization and Mapping 一种异构CPU-FPGA同步定位和映射系统
Weiyi Zhang, Liting Niu, Chaoyang Ding, Yiyang Wang, F. Farrukh, Chun Zhang
Simultaneous localization and mapping (SLAM) is an emerging robotic technology enabling autonomous robots to self-localize and map the surrounding environments. However, SLAM system is time and resource consuming, making it hard to implement on mobile devices. In this work, ROPY-SLAM, a novel heterogeneous framework for SLAM is proposed to take advantage of various devices such as personal computer and FPGA. The proposed system reduces the execution time by 28.6% and resource utilization by at most 71.2%. Moreover, ROPY-SLAM supports more flexible hardware and software implementations, providing a solution for distributed calculation of robotic applications.
同时定位和绘图(SLAM)是一种新兴的机器人技术,使自主机器人能够自我定位和绘制周围环境。然而,SLAM系统耗时耗力,难以在移动设备上实现。本文提出了一种利用个人计算机和FPGA等多种设备的异构SLAM框架——ROPY-SLAM。该系统最多可减少28.6%的执行时间和71.2%的资源利用率。此外,ROPY-SLAM支持更灵活的硬件和软件实现,为机器人应用的分布式计算提供了解决方案。
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引用次数: 0
A passive balancing algorithm for multi-cells 一种多单元无源平衡算法
Kaikai Wu, Hongyi Wang, Shucai Wang
For reducing the impact of large voltage difference between batteries in battery packs, a battery cell balancing algorithm is proposed in this paper. Based on the 0.18 µm process, the algorithm has been integrated into a 7-cell lithium battery charge and discharge protection chip. The test results show that the algorithm can reliably reduce the voltage difference between batteries and help to prolong the service life of battery packs.
为了减少电池组中电池间电压差过大的影响,本文提出了一种电池单体平衡算法。该算法基于0.18µm工艺,已集成到一款7芯锂电池充放电保护芯片中。试验结果表明,该算法能够可靠地减小电池之间的电压差,有助于延长电池组的使用寿命。
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引用次数: 1
A 210nA Quiescent Current Bandgap Reference with 5mA Load Capability Using Shared Error Amplifier 基于共享误差放大器的5mA负载能力的210nA静态电流带隙基准电路
Binwei Yang, Renwei Chen, Chenchang Zhan
This paper presents an ultra-low quiescent current bandgap reference (BGR) with current loading capability. Based on sharing an error amplifier (EA) between the BGR core and the power transistor driving circuit, the maximum load current of the BGR is extended to 5mA without relying on an output buffer. The BGR uses a dual-clamping structure to achieve low temperature coefficient (TC) and improved DC regulation. The proposed BGR is designed in a standard 0.18-µm CMOS process. Measurement results show that the input voltage range is 1.68V -2V while the output voltage is 1.19V. The quiescent current is 210nA with a maximum load current of 5mA. In the temperature range of - 40°C to 125°C, the TC of the output voltage is 9.7ppm/ °C and 30. 76ppm/ °C at the light and heavy loads, respectively. Good line and load regulations are also achieved.
提出了一种具有负载电流能力的超低静态电流带隙基准(BGR)。基于在BGR核心和功率晶体管驱动电路之间共享一个误差放大器(EA),在不依赖输出缓冲器的情况下,BGR的最大负载电流扩展到5mA。BGR采用双箝位结构,实现了低温度系数(TC)和改进的直流调节。所提出的BGR采用标准的0.18µm CMOS工艺设计。测量结果表明,输入电压范围为1.68V ~ 2v,输出电压范围为1.19V。静态电流为210nA,最大负载电流为5mA。在- 40℃~ 125℃的温度范围内,输出电压的TC值为9.7ppm/℃,30。轻负荷和重载下分别为76ppm/°C。也达到了良好的线路和负载规定。
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引用次数: 0
Efficient AVS3 Intra Prediction Hardware Design for Real-time Applications 面向实时应用的高效AVS3内部预测硬件设计
Yucheng Jiang, Hai-Jun Guo, Junhao Zheng, Jingsheng Wang, Songping Mai
a hardware-efficient hybrid greedy CU (coding unit) partition algorithm for AVS3 intra prediction, which has advantages over the traditional regression algorithm on both scheduling complexity and resource consumption, is presented. Compared with the NVidia hardware acceleration of HEVC, the proposed algorithm achieves 21% performance improvement on AI (all-intra) configuration for UHD 4K video encoding.
提出了一种用于AVS3帧内预测的硬件高效混合贪心编码单元划分算法,该算法在调度复杂度和资源消耗方面都优于传统的回归算法。与NVidia硬件加速的HEVC相比,该算法在AI (all-intra)配置下对UHD 4K视频编码的性能提升21%。
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引用次数: 0
A 1000 fps Spiking Neural Network Tracking Algorithm for On-Chip Processing of Dynamic Vision Sensor Data 一种用于片上动态视觉传感器数据处理的1000 fps脉冲神经网络跟踪算法
Chi Zhang, Lei Kang, Xu Yang, Guanghao Guo, P. Feng, Shuangming Yu, Liyuan Liu
Dynamic vision sensor (DVS), an event-based camera, has attracted significant attention due to its unique characteristics. Unlike frame-based cameras, the data format of DVS makes it difficult for traditional algorithms to process it directly. On the other hand, as a new type of brain-like neural network, the spiking neural network is specially used to process spiking data, and it is well suited for this type of event-based camera. In addition, because of the rapid development of neuromorphic hardware in recent years, it is possible to deploy SNN applications on edge-side system-on-chip. Therefore, based on the characteristics of dynamic vision sensors, this paper designs a spike encoding module and an SNN for processing sensor information. We use selective search to accomplish object tracking by classifying targets and backgrounds. The SNN can achieve 98.66% classification accuracy on our synthetic test dataset, and the tracking algorithm can achieve over 1000 fps after quantizing and compiling the network to the hardware simulator.
动态视觉传感器(DVS)作为一种基于事件的相机,因其独特的特性而备受关注。与基于帧的摄像机不同,分布式交换机的数据格式使得传统算法难以直接处理。另一方面,脉冲神经网络作为一种新型的类脑神经网络,专门用于处理脉冲数据,非常适合这类基于事件的相机。此外,由于近年来神经形态硬件的快速发展,使得在边缘端片上部署SNN应用成为可能。因此,本文根据动态视觉传感器的特点,设计了尖峰编码模块和SNN对传感器信息进行处理。通过对目标和背景进行分类,利用选择性搜索实现目标跟踪。SNN在我们的合成测试数据集上可以达到98.66%的分类准确率,跟踪算法在将网络量化并编译到硬件模拟器后可以达到1000 fps以上。
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引用次数: 1
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2022 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)
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