Device-Circuit Optimization for Minimal Energy and Power Consumption in CMOS Random Logic N

合集下载

optimization优化方法

optimization优化方法

A
2r 2
2rh
2r 2
2r
V r 2
2r 2 2 V
r
To see the minimum value of the
Area,we
A' (r )
get
4r
2V r2
We have
2r h
So the total surface area of the can
Is thus a minimum when its height
Area= area of top+ area of bottom+ curved surface area 2r2 2rh
Solution:
The volume of the can is V r2h
So, h V
r 2
Take it into the equation of Area
We just need to find the minimize the square of d(x)
f (x) d (x)2 (x 3)2 x4
y (x 3)2 x4
y (x 3)2 x4
To find the minimum value of f,we have
f '(x) 2(x 3) 4x3
Example 3 Designing a Can Using a Minimum Amount of Material
the volume of a can equals V cubic inches, where V is a constant. Find the proportions of the can that minimize the total surface area.

集成电路设计中的算法优化与实践

集成电路设计中的算法优化与实践

集成电路设计中的算法优化与实践随着信息技术的不断进步,我们的世界正变得越来越数字化。

而在这个数字化的时代,集成电路(Integrated Circuit, IC)已经成为我们日常生活中必不可少的一部分,尤其是在高科技的领域中。

集成电路主要是由数百万个微小的晶体管组成,构成了电子设备中各种复杂的电路,如计算机芯片、微波电路、数字信号处理器等等。

与此同时,为了更好地发挥集成电路的性能,各种算法也开始登场。

那么,本文将介绍集成电路设计中的算法优化与实践。

一、背景知识在介绍算法优化的基础上,我们需要了解一些背景知识。

现代的计算机由计算器(ALU)、寄存器、控制器等组成,这些单元都是由大量的逻辑门和基本单元组成的。

其中,逻辑门可以实现并、或、非等逻辑运算。

一般而言,逻辑门可以通过门电路(Gate)实现。

集成电路的设计方法主要有两种:可编程逻辑设计(Programmable Logic Design, PLD)和标准单元库设计(Standard Cell Design)。

可编程逻辑设计中使用的主要是通用门阵列(Generic Array Logic, GAL)和可编程逻辑器件(Programmable Logic Device, PLD)。

标准单元库设计采用的是一系列的标准单元,其中包括与门、或门、异或门等。

在工业中,集成电路设计通常采用硬件说明语言,如VHDL、Verilog等,进行模块化设计。

模块化设计使设计过程更容易,同时更快、更简单、更透明、更改基础方式和接口适合硬件设备。

在设计时,通常会进行的任务包括主系统的分解、模块的定义、模块之间的界面和通信协议定义和I/O 状态机的设计。

二、算法优化在集成电路的设计过程中,算法优化是必不可少的一步。

目前,主要的算法优化方法包括:Simulated Annealing Algorithm(模拟退火算法)、Genetic Algorithm(遗传算法)、Particle Swarm Optimization(粒子群算法)等。

基于YOLOv5s的轻量化行人检测算法

基于YOLOv5s的轻量化行人检测算法

现代电子技术Modern Electronics TechniqueNov. 2023Vol. 46 No. 222023年11月15日第46卷第22期0 引 言行人检测一直都是计算机视觉和目标检测领域的研究热点,其广泛应用于人群计数、智能安防、自动驾驶等应用领域。

目前存在的行人检测算法已经取得较好的检测效果,但依然面临一些挑战,比如:行人检测识别系统普遍都安装在智能汽车和无人机等移动端智能平台中,对模型轻量化要求较高,然而已有算法很难在轻量化和精度上达到平衡,因此提高行人检测精度的同时保持模型轻量化有着重要的研究意义。

常用的行人检测方法主要分为传统行人检测方法和基于深度学习的行人检测方法两大类。

基于深度卷积神经网络的目标检测算法主要分为单阶段算法和双阶段算法[1]。

其中双阶段算法是基于候选框的方法,代表算法主要有R⁃CNN [2]、Fast R⁃CNN [3]、Faster R⁃CNN [4]等,该类算法精度较高,但检测速度较慢;单阶段算法是基于回归的方法,不再提取候选区域,代表算法主要有SSD (Single Shot MultiBox Detector )[5]系列、YOLO (You Only Look Once )[6⁃9]系列等,该类算法检DOI :10.16652/j.issn.1004⁃373x.2023.22.026引用格式:高英,吴玉虹.基于YOLOv5s 的轻量化行人检测算法[J].现代电子技术,2023,46(22):151⁃158.基于YOLOv5s 的轻量化行人检测算法高 英, 吴玉虹(昆明理工大学 信息工程与自动化学院, 云南 昆明 650500)摘 要: 行人检测系统普遍安装在移动智能设备上,而这些设备对模型的轻量化要求较高,已有算法很难在精度和轻量化之间达到平衡。

针对这一问题,提出一种改进的YOLOv5s 轻量化行人检测模型。

选用EIoU 作为边界框损失函数,加速收敛并提高回归精度;结合CA (Coordinate Attention )注意力模块改进主干网络的C3模块,增强模型对行人目标的精确定位能力;引入一种新卷积层GSConv 替换颈部网络的卷积层(Conv ),以减轻模型的复杂度并保持准确性;引入改进的自注意力模块CoT ,进一步提高网络模型的特征表达能力。

powell法例题

powell法例题

powell法例题Powell's method is a numerical optimization algorithm used to find the local minimum of a function. It is an iterative method that belongs to the family of directsearch methods, which means it does not require the computation of derivatives. Powell's method is particularly useful for optimizing functions that are not smooth or have a large number of variables.The basic idea behind Powell's method is to performone-dimensional minimizations along directions defined bythe coordinate axes. The algorithm then updates the search directions based on the results of these minimizations.This allows Powell's method to efficiently explore the search space and converge to a local minimum.To illustrate Powell's method, let's consider a simple example. Suppose we want to minimize the following function:f(x, y) = (x+10y)^2 + 5(z-w)^2 + (y-2z)^4 + 10(x-w)^4。

信息存储技术课堂报告-忆阻器交叉点阵列的非理想特性与解决方案

信息存储技术课堂报告-忆阻器交叉点阵列的非理想特性与解决方案

互联电阻压降会导致交叉
and
互联电阻压降会损害读裕
阵列中每个单元上的有效
度并使感应电路的设计变
电压分布不均匀、写入延
得复杂。
迟不均匀、潜电流不均匀。
1.C. Wang, D. Feng, J. Liu, W. Tong, B. Wu, and Y. Zhang. 2017. DAWS: Exploiting crossbar characteristics for improving write performance of high density resistive memory. In Proceedings of the IEEE International Conference on Computer Design (ICCD’17). 281–288. 2. M. A. Zidan, A. M. Eltawil, F. Kurdahi, H. A. H. Fahmy, and K. N. Salama. 2014. Memristor multiport readout: A closed-form solution for sneak paths. IEEE Trans. Nanotechnol. 13, 2 (2014), 274–282.
Flip-N-Write的伪代码 Flip-N-Write实例
1.W. Wen, L. Zhao, Y. Zhang and J. Yang, "Speeding up crossbar resistive memory by exploiting in-memory data patterns," 2017 IEEE/ACM International Conferenceon Computer-Aided Design (ICCAD), Irvine, CA, 261-267. 2.Y. Zhang, D. Feng, W. Tong, Y. Hua, J. Liu, Z. Tan, C. Wang, B. Wu, Z. Li, and G. Xu. 2018. CACF: A novel circuit architecture co-optimization framework for improving performance, reliability and energy of ReR main memory system. ACM Trans. Archit. Code Optim. 15, 2 (2018). 3.W. Wen, L. Zhao, Y. Zhang and J. Yang, "Exploiting In-memory Data Patterns for Performance Improvement on Crossbar Resistive Memory," in IEEE Transactions on Computer-Aided Design of Integrated Circuits a Systems.

LM和BFGS算法的性能分析与比较-毕业论文

LM和BFGS算法的性能分析与比较-毕业论文

---文档均为word文档,下载后可直接编辑使用亦可打印---摘要数值优化是机器学习的重要部分,不断研究和改进已有的优化算法,使其更快更高效,是机器学习领域的一个重要研究方向。

作为数值优化算法中具有代表性的两个二阶算法,LM和BFGS算法各有优缺点,对它们的性能进行分析和比较给二阶数值算法的改进及更广泛的应用提供重要参考。

本论文从LM和BFGS算法的数学基础开始阐述,通过对比两个算法求解多个函数极小值的问题,我们发现LM算法和BFGS算法的差异并不大。

大多数情况下LM算法能够达到更小的误差,但是迭代次数比BFGS算法稍多。

对于等高线为椭圆的函数,LM算法的收敛速度通常比BFGS算法快,但是后期运算的迭代次数比BFGS 算法多;而其他情况下LM算法和BFGS算法的收敛速度差别不大。

由于LM算法在大部分情况下的极值求解效率稍高,我们实现了基于LM算法在前向神经网络中的学习,并用于解决模式分类问题。

实验结果表明基于LM算法的前向神经网络在垃圾邮件分类应用中能取得90%以上的分类正确率。

关键词:数值优化,LM算法,BFGS算法,前向神经网络AbstractNumerical optimization is an important part of machine learning. The analysis study of existing optimization algorithms to make them faster and more efficient is an important research direction in the field of machine learning. As two popular second-order algorithms, the LM and BFGS algorithms have their own advantages and disadvantages. The analysis and comparison of their performance have great significance for the improvement of the second-order numerical algorithms and their wider application in engineering areas.This thesis starts from introducing the mathematical foundation of LM and BFGS algorithms. By comparing the performance of the two algorithms for finding the minima of different functions, we find that the LM and BFGS algorithms have similar performance for numerical optimization problems. In most cases of our experiments, the LM algorithm can achieve smaller error, but the number of iterations is slightly higher than that of the BFGS algorithm. For the functions with elliptical contours, the convergence speed of the LM algorithm is usually faster than that of the BFGS algorithm, but the iterations of later computation are much more than those of the BFGS algorithm. while in other cases,their convergence speed is almost the same. Because of the higher efficiency of the LM algorithm in most cases, the LM algorithm is employed to train feedforward neural networks which are applied to deal with some pattern classification problem. The experimental results show that the feedforward neural network trained by the LM algorithm can reach more than 90% classification accuracy in the applications of classify spam and none spam email.Keywords:Numerical optimization,LM algorithm,BFGS algorithm,Feedforward neural networks第一章绪论1.1研究背景优化算法是用来求解问题的最优解或近似最优解的[15]。

纹理物体缺陷的视觉检测算法研究--优秀毕业论文

纹理物体缺陷的视觉检测算法研究--优秀毕业论文

摘 要
在竞争激烈的工业自动化生产过程中,机器视觉对产品质量的把关起着举足 轻重的作用,机器视觉在缺陷检测技术方面的应用也逐渐普遍起来。与常规的检 测技术相比,自动化的视觉检测系统更加经济、快捷、高效与 安全。纹理物体在 工业生产中广泛存在,像用于半导体装配和封装底板和发光二极管,现代 化电子 系统中的印制电路板,以及纺织行业中的布匹和织物等都可认为是含有纹理特征 的物体。本论文主要致力于纹理物体的缺陷检测技术研究,为纹理物体的自动化 检测提供高效而可靠的检测算法。 纹理是描述图像内容的重要特征,纹理分析也已经被成功的应用与纹理分割 和纹理分类当中。本研究提出了一种基于纹理分析技术和参考比较方式的缺陷检 测算法。这种算法能容忍物体变形引起的图像配准误差,对纹理的影响也具有鲁 棒性。本算法旨在为检测出的缺陷区域提供丰富而重要的物理意义,如缺陷区域 的大小、形状、亮度对比度及空间分布等。同时,在参考图像可行的情况下,本 算法可用于同质纹理物体和非同质纹理物体的检测,对非纹理物体 的检测也可取 得不错的效果。 在整个检测过程中,我们采用了可调控金字塔的纹理分析和重构技术。与传 统的小波纹理分析技术不同,我们在小波域中加入处理物体变形和纹理影响的容 忍度控制算法,来实现容忍物体变形和对纹理影响鲁棒的目的。最后可调控金字 塔的重构保证了缺陷区域物理意义恢复的准确性。实验阶段,我们检测了一系列 具有实际应用价值的图像。实验结果表明 本文提出的纹理物体缺陷检测算法具有 高效性和易于实现性。 关键字: 缺陷检测;纹理;物体变形;可调控金字塔;重构
Keywords: defect detection, texture, object distortion, steerable pyramid, reconstruction
II

Finding community structure in networks using the eigenvectors of matrices

Finding community structure in networks using the eigenvectors of matrices
Finding community structure in networks using the eigenvectors of matrices
M. E. J. Newman
Department of Physics and Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109–1040
We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity” over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in neteasure that identifies those vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks.

量子化学中的主要近似

量子化学中的主要近似

量子化学中的主要近似量子化学中的主要近似:1. 单Slater行列式近似2. 自洽场近似3. MO-LACO近似各种近似表示列表:零级一级高级1. 波函数单slater行列式多行列式稳定分子 CI CASSCF2. Hamilton量 HF MP2,MP4 QCISD CASPT2RHF,UHF CCSD3. DFT 局域密度近似(LDA) GGA(B3LYP,BLYP,PBE)4. MO-LCAO STO-3G 6-31g系列 aug-cc-pVDZ系列密度泛函中的单Slater行列式密度泛函中的单Slater行列式1.密度泛函的波函数是无相互作用的波函数,仅仅是为得到电子密度而引入r(r)=|y(r)*y(r)|2.绝大多数情况下只需要单Slater行列式3.含过渡金属体系的计算,可引入非整数电子填充方式,即按照能量的指数衰减函数把电子填入轨道,比如HOMO 填1.6,LUMO填0.4个电子等等Hamilton量的近似Hamilton量的近似1.Schrodinger方程是多体作用的方程,其Hamilton算符H是多体相互作用的算符。

2. Hatree-Fock算符F是单电子算符。

核与其它电子对它的作用都用一个等效势能来代替。

3.单电子的Schrodinger方程是可以计算的。

4.要使整个多电子体系在单电子“各自为政”情况下合理共存,要使用自洽场(SCF)方法密度泛函理论密度泛函理论(DFT)的近似1 基本原理:体系的基态能量由密度唯一确定。

2 基本方程:Kohn-Sham方程3 E[r(r)]=E动能[r(r)]+E静电[r(r)]+EXC[r(r)]4 动能项和静电项都与HF方法一样,不同之处在于交换相关项EXC[r(r)]交换相关泛函1 局域密度近似(LDA):EXC[r(r)]2 梯度校正(GGA): EXC[r (r), Ñ r (r) ]3 梯度校正并未增加很多计算量,因此一般都使用到梯度校正4 常用泛函有:B3LYP(杂化),BLYP,PBE5 所有泛函都包含几个经验参数,由小分子拟合得到。

东北大学本科毕业设计论文《基于支持向量机算法的电网故障诊断方法研究》

东北大学本科毕业设计论文《基于支持向量机算法的电网故障诊断方法研究》

ABSTRACT
With electricity demand growth and technology progress, power grid has become larger and more complex. Due to the formation of large power grids, the quality of electricity supply and electric security improves, also, resources complementary has been strengthened. Once fault occurs, however, it will spread to a wider area with a faster speed. For these merits, this study focuses on the fault diagnosis for power network based on support vector machine. By analyzing relative literatures and building a simulation model, this thesis finishes the analyzing of fault waveforms and harmonic distribution, and studies fault characteristics from the perspective of signal synthesis. To extract fault features submerged in original fault data, this thesis deeply studies the fuzzy processing method, the value detection of instantaneous current and the common fault feature extraction method based on wavelet singular entropy. For the error-prone of instantaneous current detection, fuzzing set ideas is drew to optimize the training samples and by modifying diagnostic strategies, the shortcoming is overcame. To reduce the elapsed time of the common fault feature extraction method based on wavelet singular entropy, a new fault feature combination is proposed by comparing the method with instantaneous current detection. This new combination can inspect faults rapidly when current has a sharp rise such as no- load line closing serious short circuit and improve the diagnostic accuracy when fault current rise is more gentle by taking advantage of wavelet transform which has a wealth of information. Under the condition that the fault features are extracted entirely, artifirt vector machine are used to diagnose power network faults. On one hand, a comparison of the two methods and a study on kernels, multi-class classification methods and SVM training algorithms are carried out. On the other hand, for a figurative expression of the diagnostic results, two dimensions are constructed from the training samples and a twodimensional optimal hyperplane is established by analyzing simulation system structure and data characteristics. Finally, by analyzing the spatial distribution of sample points, the three-dimensional optimal hyperplane is explored. -III-

模拟退火算法英文实验报告

模拟退火算法英文实验报告

模拟退火算法英文实验报告Simulated Annealing Algorithm: An Experimental ReportIn the realm of optimization problems, the simulated annealing algorithm stands as a powerful heuristic technique inspired by the annealing process in metallurgy. This report presents an experimental study on the simulated annealing algorithm, detailing its implementation, performance, and comparison with other optimization methods.IntroductionThe simulated annealing algorithm was first introduced by Kirkpatrick, Gelatt, and Vecchi in 1983 as a probabilistic technique for finding an approximate solution for a given optimization problem. It is particularly useful for complex problems where traditional methods such as gradient descent may fail to find the global minimum due to local minima.MethodologyThe experiment was conducted using a Python implementation of the simulated annealing algorithm. The objective function chosen for the experiment was a multimodal function, which is known to be challenging for optimization algorithms. The algorithm parameters, including the initial temperature, cooling rate, and stopping criteria, were carefully chosen to ensure a thorough exploration of thesolution space.ResultsThe algorithm was run multiple times with differentinitial conditions to evaluate its robustness. The results demonstrated that the simulated annealing algorithm was able to find near-optimal solutions consistently. The convergence rate and the quality of the solutions were analyzed, and the algorithm showed a gradual improvement in performance as the temperature decreased.Comparison with Other AlgorithmsThe performance of the simulated annealing algorithm was compared with other optimization techniques such as hill climbing and genetic algorithms. The comparison revealed that while hill climbing was faster, it was prone to getting stuck in local minima. On the other hand, the simulated annealing algorithm, with its probabilistic acceptance of worse solutions, was able to escape local minima and find better solutions overall.DiscussionThe simulated annealing algorithm's performance is highly dependent on its parameters. The choice of initial temperature and cooling schedule significantly affects the algorithm's ability to explore the solution space and converge to a good solution. The experiment highlighted the importance of parameter tuning in achieving optimal results.ConclusionThe simulated annealing algorithm proved to be aneffective tool for solving complex optimization problems. Its ability to balance exploration and exploitation makes it a versatile choice for a wide range of applications. Futurework could involve refining the parameter selection process and exploring its applicability to real-world problems.References1. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.2. Cerny, V. (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45(1), 41-51.This report concludes with a brief overview of the simulated annealing algorithm's application and its potential for further research and practical use in optimization problems.。

OPTIMIZATION DEVICE AND METHOD OF CONTROLLING OPTI

OPTIMIZATION DEVICE AND METHOD OF CONTROLLING OPTI

专利名称:OPTIMIZATION DEVICE AND METHOD OFCONTROLLING OPTIMIZATION DEVICE发明人:Takeshi Mishina,Satoshi MATSUURA申请号:US17178290申请日:20210218公开号:US20210173990A1公开日:20210610专利内容由知识产权出版社提供专利附图:摘要:An optimization device includes: a memory; and a processor configured to:calculate, as bit operations, when any bit in a bit string representing a state of an Ising model is inverted, an energy change value of the Ising model based on a coefficientindicating magnitude of an interaction between an own bit and the inverted bit in the bit string; output a first signal indicating inversion availability of the own bit according to the energy change value and a second signal indicating the energy change value; select the bit to be inverted in the bit string and the energy change value corresponding to the bit based on the first signal and the second signal; output a fourth signal indicating the selected energy change value; and calculate energy of the Ising model based on the energy change value indicated by the fourth signal.申请人:FUJITSU LIMITED地址:Kawasaki-shi JP国籍:JP更多信息请下载全文后查看。

英语作文-集成电路设计中的优化算法与设计方法解析

英语作文-集成电路设计中的优化算法与设计方法解析

英语作文-集成电路设计中的优化算法与设计方法解析In the field of integrated circuit design, optimization algorithms and design methods play a crucial role in improving the performance and efficiency of circuits. These algorithms and methods aim to minimize power consumption, maximize speed, and enhance reliability. In this article, we will analyze the various optimization algorithms and design methods used in integrated circuit design.One commonly used optimization algorithm in integrated circuit design is the Genetic Algorithm (GA). GA is inspired by the process of natural selection and evolution. It starts with an initial population of potential solutions and applies genetic operators such as selection, crossover, and mutation to generate new solutions. Through successive generations, the algorithm converges towards the optimal solution. GA has been successfully applied in various aspects of integrated circuit design, including floorplanning, placement, routing, and logic synthesis.Another widely used optimization algorithm is the Simulated Annealing (SA) algorithm. SA is based on the annealing process of cooling and slowly heating a material to reduce defects and improve its properties. In the context of integrated circuit design, SA starts with an initial solution and iteratively explores the solution space by accepting worse solutions with a certain probability. This allows the algorithm to escape local optima and converge towards a global optimum. SA has been applied to problems such as placement, routing, and timing optimization in integrated circuit design.In addition to optimization algorithms, design methods also play a crucial role in integrated circuit design. One commonly used design method is the Register-Transfer Level (RTL) design. RTL design focuses on capturing the behavior of a circuit using a hardware description language such as Verilog or VHDL. It allows designers to specify the functionality of the circuit at a higher level of abstraction before the actualimplementation. RTL design enables efficient circuit exploration and optimization before the physical design stage.Another important design method is High-Level Synthesis (HLS). HLS allows designers to describe the circuit's behavior using a high-level programming language such as C or C++. The HLS tool then automatically generates the corresponding hardware implementation. This design method enables designers to explore different architectural optimizations and trade-offs at a higher level of abstraction. HLS has been widely used in the design of digital signal processing circuits and complex system-on-chip designs.Furthermore, in the era of deep learning and artificial intelligence, optimization algorithms and design methods have also been applied to the design of specialized hardware for accelerating neural networks. Techniques such as neural architecture search, quantization, and pruning have been developed to optimize the performance and energy efficiency of neural network accelerators.In conclusion, optimization algorithms and design methods are essential in integrated circuit design. They enable designers to improve the performance, efficiency, and reliability of circuits. Genetic algorithms, simulated annealing, RTL design, and high-level synthesis are just a few examples of the many techniques used in integrated circuit design. As technology advances, new algorithms and methods will continue to emerge, pushing the boundaries of integrated circuit design further.。

Optimization第二讲

Optimization第二讲
Faculty of Economics
Optimization Lecture 2
Marco Haan February 21, 2005
Last week
• Optimizing a function of more than 1 variable. • Determining local minima and local maxima. • First and second-order conditions. • Determining global extrema with direct restrictions on variables.
2x1 + 3x2 = 10. In this case: f(x1,x2) = x1½ x2½ and g(x1,x2) = 10 – 2x1 – 3x2.
3
We want to maximize f(x1,x2) subject to g(x1,x2) = 0.
Suppose that, from g(x1,x2) = 0 we can write x2 = γ(x1).
max f (x1, x2 ) max f (x1, (x1)) (x1).
'(x1) f1(x1*, (x1*)) f2 (x1*, (x1*)) '(x1*) 0.
f1(x1*, x2*) f2 (x1*, x2*)
g1(x1*, x2*) g2 (x1*, x2*)
We’ve seen this in Micro 1!
Take the total differential: dx2 = γ’(x1 ) dx1
Also: g1(x1, x2) dx1 + g2(x1, x2) dx2 = 0

低开销三进制域Eta双线性对硬件加速器

低开销三进制域Eta双线性对硬件加速器
Tel/Fax: +86-10-82789618
低开销三进制域 Eta 双线性对硬件加速器*
李翔宇
清华大学 微电子学研究所 北京信息科学与技术国家研究中心, 北京 100084 通信作者: 李翔宇, E-mail: xiangyuli@
摘 要: 基于身份标识的加密 (Identity-Based Encryption, IBE) 在物联网领域有很高的潜在应用价值, 双线性对运算是其中的关键运算. 本文针对物联网需求设计了一种低开销的双线性对硬件加速器. 它选择 了水线低执开行销,的增超加奇了异电椭路圆的曲吞线吐上率的. 三M进ille制r 域算法et硬a 件对实. 该现设中计通将过将MiMlleilrle算r 循法环与中幂的运算GF分(为36两m)个上硬的件稀部疏分乘, 流法 与立方结合并重新调度和优化, 减少中间值相关的开销. 优化后的方案, 具有更简单的运算单元, 减少了寄 存制器较的复使杂,用采和用存了储微器码的控读制写的. 方核式心进模行乘实运现算.采本用文了选最取高定位义优在先的GF字(串39行7)结上构的. 椭考圆虑曲到线电上路的的e规ta模对较进大行, 了控 ASIC 实现, 在 90 nm 工艺下, 版图面积 650 × 650 µm2, 计算时间为 16.7 µs, 面积延时积比现有 eta 对 ASIC 实现减小了 38.8% . 关键词: 基于身份标识的加密; 双线性对; 专用集成电路; eta 对 中图分类号: TN495 文献标识码: A DOI: 10.13868/ki.jcr.000445
2015 年, Chung 等人 [10] eta 对 ASIC 硬件加速器.
3 Eta 对和 Miller 算法
Eta 对是定义在三进制域 GF (3m) 超奇异椭圆曲线 y2 = x3 − x + b 上的对称双线性对, 其中参数 b ∈ {1, −1}. 目前普遍采用的三进制 eta 对的计算方法是无立方根的逆向 eta 对算法, 如算法 1 所示:

fitting inititial values

fitting inititial values

fitting inititial valuesFitting initial values refers to the process of estimating or determining the starting values of parameters in a model or optimization problem. These initial values are used as a starting point for iterative algorithms that aim to find the optimal values of the parameters.The choice of appropriate initial values can have a significant impact on the performance and convergence of the optimization process. Poor initial values may lead to slow convergence, convergence to local minima instead of the global optimum, or even failure to converge at all.There are several methods for fitting initial values, depending on the specific problem and the available information. Here are some common approaches:1. Random initialization: Choosing random values within a reasonable range as the initial values. This can be useful when little prior knowledge is available about the最优 values.2. Historical data or empirical estimates: Utilizing existing data or prior knowledge to provide initial guesses. For example, using historical averages, trends, or correlations to initialize parameters in a time-series model.3. Expert knowledge or domain expertise: In some cases, experts may have insights or intuitions about plausible initial values based on their experience and understanding of the problem.4. Gradient-based initialization: If the objective function is differentiable, using the gradient information to initialize the parameters in a direction that leads to improvement.5. Initialization from previous runs or similar problems: If similar optimization problems have been solved before, the final or converged values from those previous runs can be used as initial values for the current problem.It is often beneficial to experiment with different initial value configurations and assess their impact on the optimization process. Some optimization algorithmsmay have built-in mechanisms for automated or adaptive initialization. Additionally, iterative refinement of initial values during the optimization process is sometimes employed to improve the chances of finding the optimal solution.。

基于关键路径与改进遗传算法的最佳占空比求解

基于关键路径与改进遗传算法的最佳占空比求解

基于关键路径与改进遗传算法的最佳占空比求解徐辉;李丹青;应健锋;李扬【摘要】纳米工艺下,负偏置温度不稳定性(NBTI)成为影响电路老化效应的主导因素.多输入向量控制(M-IVC)是缓解由于NBTI效应引起电路老化的有效方法,而M-IVC的关键是最佳占空比的求解.在充分考虑时序余量的设计与电路实际操作情况下,对电路采用了静态时序分析,精确定位电路中关键路径.对关键路径采用改进的自适应遗传算法求解最佳占空比.实验结果表明:在时序余量为5%时,电路的平均老化率相比现有方案降低了1.49%,平均相对改善率为18.29%.%Under the nanometer process,the negative bias temperature instability (NBTI) is the dominant factor affecting the aging effect of the circuit. Multi input vector control (M-IVC) is an effective method to mitigate the NBTI effect,and the key to M-IVC is the solution of the optimal duty cycle. After fully considering the original design of the time margin and the actual operation of the circuit,the static timing analysis is used to accurately locate the critical path in the circuit. The improved adaptive genetic algorithm is used to solve the critical path for the best duty cycle. The experimental results show that when the time margin is 5%,the average aging rate of the circuit is reduced by 1. 49% compared with the existing scheme,and the average relative improvement rate is 18. 29%.【期刊名称】《传感器与微系统》【年(卷),期】2017(036)010【总页数】5页(P124-128)【关键词】集成电路;老化效应;最佳占空比;负偏置温度不稳定性;多输入向量控制;遗传算法【作者】徐辉;李丹青;应健锋;李扬【作者单位】安徽理工大学计算机科学与工程学院,安徽淮南232001;安徽理工大学计算机科学与工程学院,安徽淮南232001;合肥工业大学电子科学与应用物理学院,安徽合肥230009;江苏商贸职业学院信息系,江苏南通226011【正文语种】中文【中图分类】TN407应集成电路的发展需求,晶体管的特征尺寸不断按比例缩放,使得负偏置温度不稳定性(negative bias temperature instability,NBTI)成为引起电路老化的主导因素[1]。

一种改进的类电磁机制算法

一种改进的类电磁机制算法

一种改进的类电磁机制算法苗苗【期刊名称】《计算机与数字工程》【年(卷),期】2012(40)6【摘要】文章以无约束优化问题为研究对象,分析了类电磁机制算法的原理,即模拟了电磁场中带电粒子之间的吸引排斥机制,通过该机制使得粒子朝着最优粒子移动.针对原算法中存在的运算量大、收敛速度慢等问题,提出了一种改进的类电磁机制算法.新算法采用归一化目标函数值方法,简化了电量计算公式;同时引入自适应移动算子,将粒子的优劣和迭代的过程体现在粒子的移动过程中,使得算法在搜索过程中能朝着更精确的解移动.实验证明,改进后的算法具有更好的收敛效果和更高的执行效率.%Aim to solve the unconstrained optimization problems, according to the problems of the original Electromagnetism-like meeha-nism(EM) algorithm, such as large computation, low convergence rate, this paper proposes an improved EM algorithm after analyzing the function optimization principle of the original algorithm By adopting the method for normalizing the objective function value, the power formula is simplified. By using an adaptive move operator, the particles have more chance to move into the other feasible regions. Experiments show that the improved algorithm has a better convergence result and higher efficiency.【总页数】3页(P36-38)【作者】苗苗【作者单位】西安工业大学西安710032;西安电子科技大学电路CAD所西安710071【正文语种】中文【中图分类】TP301.6【相关文献】1.一种改进的类电磁机制算法 [J], 姜建国;刘永青;尚海豹;林龙军;王双记2.一类改进的类电磁机制算法 [J], 郭鹏;王宇平3.改进类电磁机制算法的FSS优化设计 [J], 胡晓朋;田雨波4.一种改进的无局部搜索的类电磁机制算法 [J], 单玉乐;曾建潮;谭瑛5.基于师生交流机制的改进类电磁机制算法 [J], 吴擎; 徐惟罡; 张春江因版权原因,仅展示原文概要,查看原文内容请购买。

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This research was supported by NSF Grant No MIP-9502575
at the Georgia Institute of Technology.
power minimization is accomplished through conjoint optimization of both device and circuit designs for a speci c logic schematic, activity pro le and critical path delay requirement. In particular, we determine the optimal supply voltage for the entire module, one or more distinct threshold voltage values for groups of MOSFETs and a channel width for each transistor that minimizes total power or the total energy consumption per cycle while satisfying the critical path delay constraint. The resulting designs operate at low supply voltages and have comparable leakage and switching power dissipation components. The leakage current becomes signi cant due to the need to reduce threshold voltage with reduced supply voltage to maintain speed. The underlying optimization algorithms and the associated CAD tools allow an order of magnitude reduction in power consumption over designs optimized for only supply voltage and device widths as opposed to supply voltage, device widths and threshold voltage . The algorithms discussed in this paper can be used to design a CMOS process for ultra low power designs the algorithms in uence threshold voltage selection. In determining the threshold voltage for a process being developed for future applications, one may use the algorithms on existing benchmarks with predicted circuit timing parameters to nd the most desirable threshold voltage. Also, the methods discussed in this paper can be used to statically adjust the threshold voltage as follows. The threshold-adjust implant step 2 which produces low Vts natural nmos and pmos transistors is eliminated. A static reverse bias is applied to the p-substrate and the n-wells to obtain the desired nmos and pmos Vts values, respectively. An example of an inverter designed this way is shown in Figure 1. A key advantage of this approach is that one can design ultra low-power VLSI with existing CMOS manufacturing technology.
Abstract
We demonstrate a new approach for minimizing the total of the static and the dynamic power dissipation components in a CMOS logic network required to operate at a speci ed clock frequency using joint optimization of both device and circuit designs for a speci c logic schematic and activity pro le. We present a new approach to designing ultra low-power CMOS logic circuits by joint optimization of supply voltage, threshold voltage and device widths for a speci ed speed constraint. The static leakage and dynamic switching energy components are considered and an e cient heuristic is developed that delivers over an order of magnitude savings in power over conventional optimization methods.
Device-Circuit Optimization for Minimal Energy and Power Consumption in CMOS Random Logic Networks
Pankaj Pant Georgia Institute of Technology pant@ Vivek De Intel Corp., Hillsboro, OR vde@ Abhijit Chatterjee Georgia Institute of Technology abhijit.chatterjee@
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1 Introduction
Traditionally, approaches to minimizing the dynamic power dissipation or switching energy consumption of static CMOS logic networks have advocated straightforward reduction in the supply voltage. It has been shown that the resulting throughput loss on account of increased gate delay can be e ectively compensated through increased data-path parallelism in special-purpose signal-processing applications and by careful transistor sizing 3 . Methodologies for minimizing the sum total of static or leakage and dynamic energy consumption in general-purpose CMOS processes without regard to a performance requirement have also been proposed 2 . Total power is minimized through selection of supply and threshold voltage values such that the leakage and switching components of the dissipation are equal. The accompanying performance loss can be compensated to some degree by minimizing the product of the switching energy and the propagation delay instead of power or energy alone. The relationship between transistor sizing and power has been examined in 10 . A study of supply and threshold voltage selection for low power has been performed in 7 . However, the focus of this study is di erent from the CMOS circuit networks discussed in our paper. In this paper, we propose a comprehensive approach to the minimization of the sum total of the static and dynamic energy dissipation components in a CMOS random logic network required to operate at a speci ed clock frequency. This
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