2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS)最新文献

Deep Multiframe Enhancement for Motion Prediction in Video Compression 视频压缩中运动预测的深度多帧增强

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665523

N. Prette, D. Valsesia, T. Bianchi

引用次数: 0

An Automated Flow for Configuration and Generation of CNN based AI accelerators for HW Emulation & FPGA Prototyping 基于CNN的人工智能加速器在硬件仿真和FPGA原型中的自动配置和生成流程

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665606

Ahmed Nasser, Karim Ahmed Fadel, Karim Abbas, K. Ahmed, Mohamed Abdelsalam, Mahmoud Gaber

引用次数: 0

Delay-Based Neural Computation: Pulse Routing Architecture and Benchmark Application in FPGA 基于延迟的神经计算:脉冲路由体系结构及其在FPGA中的基准测试应用

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665468

V. Thanasoulis, B. Vogginger, J. Partzsch, C. Mayr

引用次数: 0

A Computationally Efficient Model of MEMS Stopper for Reliability Optimization 一种用于MEMS止动器可靠性优化的高效计算模型

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665627

Tianfang Peng, Zheng You

{"title":"A Computationally Efficient Model of MEMS Stopper for Reliability Optimization","authors":"Tianfang Peng, Zheng You","doi":"10.1109/icecs53924.2021.9665627","DOIUrl":"https://doi.org/10.1109/icecs53924.2021.9665627","url":null,"abstract":"MEMS stoppers are commonly used structures to prevent failures caused by mechanical overload such as shock and pressure. However, the numerical research of the stoppers could be computationally costly and non-convergent, since it involves non-linear mechanical features such as contact and collision. This poses difficulties to the reliability design and optimization of MEMS. This paper proposes a parametric model of MEMS stoppers that is computationally efficient for reliability design. The model converts the material and geometric characteristics of the stopper into a nonlinear spring system. The efficiency and convergence of numerical computation of the MEMS structure with stoppers were effectively improved through both static and transient FEM research examples. The stress distribution and transient displacement response obtained by this model were in good agreement with the calculation results of traditional contact algorithm in FEM examples. The overload-resistance of MEMS stoppers were further analyzed. Finally, we optimized the design of MEMS stopper's shape and stiffness based on the parametric model. The model proposed in this study is suitable for the design and optimization of the anti-overload structure of MEMS.","PeriodicalId":448558,"journal":{"name":"2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126708476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Rail-to-Rail CMOS Voltage Comparator with Programmable Hysteresis 具有可编程迟滞的轨对轨CMOS电压比较器

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665571

Mustafa Oz, E. Bonizzoni, F. Maloberti, Alper Akdikmen, Jianping Li

引用次数: 2

Machine learning-based acceleration of Genetic Algorithms for Parameter Extraction of highly dimensional MOSFET Compact Models 基于机器学习的遗传算法加速高维MOSFET紧凑模型参数提取

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665517

Gazmend Alia, Andi Buzo, H. Maier-Flaig, Klaus-Willi Pieper, L. Maurer, G. Pelz

{"title":"Machine learning-based acceleration of Genetic Algorithms for Parameter Extraction of highly dimensional MOSFET Compact Models","authors":"Gazmend Alia, Andi Buzo, H. Maier-Flaig, Klaus-Willi Pieper, L. Maurer, G. Pelz","doi":"10.1109/icecs53924.2021.9665517","DOIUrl":"https://doi.org/10.1109/icecs53924.2021.9665517","url":null,"abstract":"The need for more accurate simulations has pushed scientists and engineers to design better, more accurate and more complex MOSFET compact models. This has been supported by the big improvements in computational power and speed in the last decades. The number of parameters of the compact models has increased to hundreds and thousands and it is far beyond what the human mind can handle. As a results, the calibration of the models to represent the real characteristics of the device, also known as parameter extraction, is a complex and time consuming task. To solve this problem, many automatic techniques have been tried and the most promising ones are based on genetic algorithms. Genetic algorithms on the other side, although appropriate for such tasks, require a large number of simulations to converge to a good solution. In this paper we propose a methodology to drastically reduce the number of simulations by introducing a combination of genetic algorithms and surrogate models as classifiers. The state of the art about the combination of surrogate models and genetic algorithms is exclusively focused on how to use surrogate models to substitute the expensive simulations. Our novel approach consists on adding a classifier layer between the genetic algorithm and the simulations, which filters out a significant number of non-promising parameter sets that do not need to be simulated at all. In this research, differential evolution was used as the genetic algorithm and after a careful evaluation of several classifier types, the decision tree classifier was selected as the best performing one. The method was tested with two complex real life problems, BSIM4 and HiSIM-HV MOSFET compact models, and the results show that up to 70% of the simulations could be eliminated without disturbing the convergence of the algorithm and maintaining the accuracy of the solution.","PeriodicalId":448558,"journal":{"name":"2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122643395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

A Deep Learning Framework for Breast Tumor Detection and Localization from Microwave Imaging Data 基于微波成像数据的乳腺肿瘤检测与定位的深度学习框架

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665521

Salwa K. Al Khatib, Tarek Naous, R. Shubair, H. M. E. Misilmani

引用次数: 4

A Low Phase Noise Fractional-N PLL for mmWave Telecom and RADAR Applications 用于毫米波电信和雷达应用的低相位噪声分数n锁相环

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665480

N. Naskas, Nikolaos Alexiou, Spyros Gkardiakos, Aris Agathokleous, Nikos Tsoutsos, Kostas Kontaxis, George Ntounas, Giannis Kousparis

引用次数: 0

Developing AI Agent with Functional Mockup Units for Car Autonomous Navigation 基于功能实体单元的汽车自主导航AI智能体开发

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665639

Al-Dakheeli Muhammed, Hadeer Essam, Beshoy Alber, Kirolos Samuel, Hagar Muhammed, M. Wagdy, Nouran Khaled, Hadeer Fawzy, Aya Tarek, Mohamed Abdel Salam, M. El-Kharashi

引用次数: 0

Margolus Chemical Wave Logic Gate with Memristive Oscillatory Networks 具有记忆振荡网络的Margolus化学波逻辑门

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665632

Theodoros Panagiotis Chatzinikolaou, Iosif-Angelos Fyrigos, V. Ntinas, Stavros Kitsios, P. Bousoulas, Michail-Antisthenis I. Tsompanas, D. Tsoukalas, A. Adamatzky, G. Sirakoulis

{"title":"Margolus Chemical Wave Logic Gate with Memristive Oscillatory Networks","authors":"Theodoros Panagiotis Chatzinikolaou, Iosif-Angelos Fyrigos, V. Ntinas, Stavros Kitsios, P. Bousoulas, Michail-Antisthenis I. Tsompanas, D. Tsoukalas, A. Adamatzky, G. Sirakoulis","doi":"10.1109/icecs53924.2021.9665632","DOIUrl":"https://doi.org/10.1109/icecs53924.2021.9665632","url":null,"abstract":"As conventional computing systems are striving to increase their performance in order to compensate for the growing demand of solving difficult problems, emergent and unconventional computing approaches are being developed to provide alternatives on efficiently solving a plethora of those complex problems. Chemical computers which use chemical reactions as their main characteristic can be strong candidates for these new approaches. Oscillating networks of novel nano-devices like memristors are also able to perform calculations with their rich dynamics and their strong memory and computing features. In this work, the combination of the aforementioned approaches is achieved that capitalizes on the threshold switching mechanism of low-voltage CBRAM devices to establish a memristive oscillating circuitry that is able to act as a chemical reaction - diffusion system through the network nodes' interactions. The propagation of the voltage signals throughout the medium can be used to establish a mechanism for specific logic operations according to the desired logic function leading to the nano-implementation of Margolus chemical wave logic gate.","PeriodicalId":448558,"journal":{"name":"2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123603592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2