nils-herrmann/clean_scirepeval huggingface dataset:
Power-Optimal Simultaneous Buffer Insertion/Sizing and Wire Sizing

This paper studies the problems of minimizing power dissipation of an interconnect wire by simultaneously considering buffer insertion/sizing and wire sizing (BISWS). We consider two cases, namely minimizing power dissipation with optimal delay constraints, and minimizing power dissipation with a given delay penalty. We derive closed form optimal solutions for both cases. These closed form solutions can be used to efficiently estimate the power dissipation in the early stages of the VLSI designs. We observe that the power dissipation can be much different even with the same optimal delay.
Behavioral modeling for analog system-level simulation by wavelet collocation method

In this paper, we propose a wavelet collocation method with nonlinear companding to generate behavioral models for analog circuits at the system level. During the overall process of circuit modeling, nonlinear function approximation is an important issue to accurately capture the nonideal input-output relations of analog circuit blocks. While a great number of previous research works focus on the high-dimensional top-down design/synthesis model, which involves large analog design spaces, this paper primarily concentrates on the bottom-up verification model requiring both simple representation and high accuracy. Taking advantage of the local support of wavelet bases, a nonlinear companding method is developed to control the modeling error distribution based on system-level simulation requirements. It, in turn, significantly improves the simulation efficiency at the system level. To demonstrate the promising features of the proposed method, two circuit examples, a fourth-order switched-current filter and a voltage-controlled oscillator, are employed to build the behavioral models.
An efficient Sylvester equation solver for time domain circuit simulation by wavelet collocation method

The Fast Wavelet Collocation Method (FWCM) is a recently proposed circuit simulation approach, which is very promising in achieving uniform error distribution and handling singularities in high-speed circuits, compared with conventional time domain simulation methods. In this paper, we aim to extend the wavelet collocation method to the simulation of large-scale circuits, where a large dimension Sylvester equation needs to be solved. The performance of the wavelet simulator is dominated by the time and memory consumption of the Sylvester equation solver. In order to improve the simulation efficiency, we propose a direct method to solve the Sylvester equation by Schur decomposition. Numerical experiments demonstrate that the proposed simulator can achieve higher computation speed and higher simulation accuracy as well as more robust convergence than SPICE.
A DSP-based turbo codec for 3G communication systems

In this paper, we present a high performance Turbo CODEC implemented using digital signal processor for wireless systems following recommended CDMA2000 standard. At the transmitter side, the Turbo encoder is implemented with a modified 15×13 odd-even interleaver. As modern DSP chips, like TI C64x, are designed with multiple functional units, it is important to fully explore the machine-level parallelism to maximize the usage of available computation sources. To this end, at the receiver side, compared to the algorithm used in TI [7], we have redesigned the decoding algorithms with reduced instruction count by 20%. Furthermore, by transforming a number of add/subtract operations to multiplication operations, our decoder can recycle a few functional units previously unused in TI [7]. This Turbo Codec is capable of encoding one frame in 3.1 microseconds and finishing one decoding stage in 18.1 microseconds on a C64x DSP operating at 400 Mhz.
Frequency domain wavelet method for high-speed circuit simulation

In this paper, a Frequency domain Fast Wavelet Collocation Method (FFWCM) is proposed for the simulation of high-speed linear VLSI circuits. Compared with the time domain Fast Wavelet Collocation Method, the proposed FFWCM solves the state equation in the frequency domain and reduces the problem unknowns by avoiding expanding all the state variables with wavelet functions. Moreover, frequency domain singularities can be easily handled by an efficient adaptive scheme and the simulation error bound is guaranteed in the frequency domain. As a result, memory space and simulation time are greatly reduced by FFWCM, which makes it efficient for simulating large scale circuits. Numerical experimental results have further demonstrated the promising features of FFWCM.
Wavelet method for high-speed clock tree simulation

In this paper, we propose a fast wavelet collocation algorithm for high-speed clock tree simulation. Taking advantage of the specific structure of clock trees and the superior computational property of wavelets, the proposed algorithm presents the following merits. (1) It can perform both transient simulation and steady-state analysis with arbitrary input. (2) It employs nonlinear buffer model and nonuniform interconnect wire model. (3) It has a low computational complexity O(N) and can deal with considerably large circuits. (4) The proposed wavelet method works in time domain so that the simulation error in time domain can be well-controlled. Numerical experiment results demonstrate the promising features of the proposed algorithm in high-speed clock tree simulations.
A frequency domain fast wavelet collocation method for high-speed circuit simulation

In this paper, we present the frequency domain fast wavelet collocation method (FFWCM) for high-speed VLSI circuit simulation. Compared with the time domain fast wavelet collocation method (TFWCM), FFWCM presents the following merits. First, it consumes much less memory space and computation time, and can effectively deal with large scale circuits. Second, the frequency domain singularity can be easily handled by adaptive schemes and a frequency domain uniform error distribution can be achieved. Furthermore, the approximated time domain analytical solution can be simply obtained without performing the time consuming numeric inverse Laplace transforms. Numerical experimental results have further demonstrated the promising features of FFWCM.
A Novel Buffer Insertion Algorithm for Clock Delay and Skew Minimization

In this paper,we propose a novel buffer insertion theory for clock delay and skew minimization.Based on the Elmore delay model,buffer to buffer delay is a convex function of buffer positions in a clock tree.The optimal buffer placement for delay minimization is achieved when all delay functions have the same derivative values.The minimal skew can be obtained by equalizing delay functions of different source to sink paths.For a given clock routing tree,we initially insert the same level of buffers in all the source to sinks paths,then minimize the clock delay by optimizing buffer positions,and minimize skew by simultaneous buffer level augment and buffer sizing.
Behavioral modeling of analog circuits by wavelet collocation method

In this paper, we develop a wavelet collocation method with nonlinear companding for behavioral modeling of analog circuits. To construct the behavioral models, the circuit is first partitioned into building blocks and the input-output function of each block is then approximated by wavelets. As the blocks are mathematically represented by sets of simple wavelet basis functions, the computation cost for the behavioral simulation is significantly reduced. The proposed method presents several merits compared with those conventional techniques. First, the algorithm for expanding input-output functions by wavelets is a general-purpose approach, which can be applied in automatically modeling of different analog circuit blocks with different structures. Second, both the small signal effect and the large signal effect are modeled in a unified formulation, which eases the process of modeling and simulation. Third, a nonlinear companding method is developed to control the modeling error distribution, To demonstrate the promising features of the proposed method, a 4th order switched-current filter is employed to build the behavioral model.
Parasitic and mismatch modeling for optimal stack generation [in CMOS]

Control of parasitic capacitance and minimization of layout mismatch are very crucial in the analog physical design automation. In this paper we study the techniques for modeling the distributed parasitic capacitance, modeling the parasitic parameter mismatch due to process gradient and modeling the inner stack routing mismatch. Based on the proposed models, a transistor folding technique and a dummy transistor insertion technique are developed to optimize the stack shape, control of parasitics and guarantee the generation of an Eulerian graph for a given diffusion graph.
Calculating the Perfect Match: An Efficient and Accurate Approach for Robot Self-localization

The paper develops a new approach for robot self-localization in the Robocup Midsize league. The approach is based on modeling the quality of an estimate using an error term and numerically minimizing it. Furthermore, we derive the reliability of the estimate analyzing the error function and apply the derived uncertainty value to a sensor integration process. The approach is characterized by high precision, robustness and computational efficiency.
CBR for State Value Function Approximation in Reinforcement Learning

CBR is one of the techniques that can be applied to the task of approximating a function over high-dimensional, continuous spaces. In Reinforcement Learning systems a learning agent is faced with the problem of assessing the desirability of the state it finds itself in. If the state space is very large and/or continuous the availability of a suitable mechanism to approximate a value function – which estimates the value of single states – is of crucial importance. In this paper, we investigate the use of case-based methods to realise that task. The approach we take is evaluated in a case study in robotic soccer simulation.
Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method

This paper introduces NFQ, an algorithm for efficient and effective training of a Q-value function represented by a multi-layer perceptron. Based on the principle of storing and reusing transition experiences, a model-free, neural network based Reinforcement Learning algorithm is proposed. The method is evaluated on three benchmark problems. It is shown empirically, that reasonably few interactions with the plant are needed to generate control policies of high quality.
Neural reinforcement learning to swing-up and balance a real pole

This paper proposes a neural network based reinforcement learning controller that is able to learn control policies in a highly data efficient manner. This allows to apply reinforcement learning directly to real plants -neither a transition model nor a simulation model of the plant is needed for training. The only training information provided to the controller are transition experiences collected from interactions with the real plant. By storing these transition experiences explicitly, they can be reconsidered for updating the neural Q-function in every training step. This results in a stable learning process of a neural Q-value function. The algorithm is applied to learn the highly nonlinear and noisy task of swinging-up and balancing a real inverted pendulum. The amount of real time interaction needed to learn a highly effective policy from scratch was less than 14 minutes.
Learning policies for abstract state spaces

Applying Q-learning to multidimensional, real-valued state spaces is time-consuming in most cases. In this article, we deal with the assumption that a coarse partition of the state space is sufficient for learning good or even optimal policies. An algorithm is presented which constructs proper policies for abstract state spaces using an incremental procedure without approximating a Q-function. By combining an approach similar to dynamic programming and a search for policies, we can speed up the learning process. To provide empirical evidence, we use a cart-pole system. Experiments were conducted for a simulated environment as well as for a real plant.
Reinforcement Learning Using a Grid Based Function Approximator

Function approximators are commonly in use for reinforcement learning systems to cover the untrained situations, when dealing with large problems. By doing so however, theoretical proves on the convergence criteria are still lacking, and practical researches have both positive and negative results. In a recent work  with neural networks, the authors reported that the final results did not reach the quality of a Q-table in which no approximation ability was used. In this paper, we continue this research with grid based function approximators. In addition, we consider the required number of state transitions and apply ideas from the field of active learning to reduce this number. We expect the learning process of a similar problem in a real world system to be significantly shorter because state transitions, which represent an object's actual movements, require much more time than basic computational processes.
Brainstormers 2D — Team Description 2009

The main focus of the Brainstormers' effort in the RoboCup soccer simulation 2D domain is to develop and to apply machine learning techniques in complex domains. In particular, we are interested in ap- plying reinforcement learning methods, where the training signal is only given in terms of success or failure. Our final goal is a learning system, where we only plug in "win the match" - and our agents learn to gener- ate the appropriate behavior. Unfortunately, even from very optimistic complexity estimations it becomes obvious, that in the soccer simulation domain, both conventional solution methods and also advanced today's reinforcement learning techniques come to their limit - there are more than (108×50) 23 different states and more than (1000) 300 different poli- cies per agent per half time. This paper outlines the architecture of the Brainstormers team, focuses on the use of reinforcement learning to learn various elements of our agents' behavior, and highlights other advanced artificial intelligence methods we are employing.
Comparing different methods to speed up reinforcement learning in a complex domain

We introduce a new learning algorithm (semi-DP algorithm) designed for MDPs (Markov decision process) where actions either lead to a deterministic successor state or to the terminal state. The algorithm only needs a finite number of loops to converge exactly to the optimal action-value function. We compare this algorithm and three other methods to speed up or simplify the learning process to ordinary Q-learning in a soccer grid-world. Furthermore, we show that different reward functions can considerably change the convergence time of the learning algorithms even if the optimal policy remains unchanged.
Effective Methods for Reinforcement Learning in Large Multi-Agent Domains (Leistungsfähige Verfahren für das Reinforcement Lernen in komplexen Multi-Agenten-Umgebungen)

Robotic soccer requires the ability of individually acting agents to cooperate. The simulation league of RoboCup therefore offers an ideal testbed for evaluating multi-agent methods. In this paper we discuss how Reinforcement Learning (RL) methods can be succesfully applied within the scenario of learning to cooperatively score a goal. Due to the complexity of the task, enhanced methods of learning have to be applied. We discuss several approaches from literature and also present an own approach. All approaches are evaluated on a discretized version of robotic soccer, which we call gridworld soccer.
Learning Symbolic Maps from Robot Navigation Bachelor Thesis in Cognitive Science

Navigation in real life inspires powerful approaches used in robotics. Giving an overview of various biological navigation systems and their influence on the field of robotics, this thesis will present an extension of such an approach by introducing a knowledge-based map learning simulation. The presented system will be enlarged to mimic the biological correlates even more realistically by simulating differently skilled agents. The focus will lie on the agent’s knowledge representation and therefore on the acquisition and maintenance of symbolic maps representing the environment the agent is operating in. The different skills of the agents will mainly be simulated by distorting their sensory perception that is equivalent to the implementation of measurement noise from the technical point of view. The presented system will be able to deal with some of the problems concerning map building that occur when distorted sensory input is integrated in the simulation. Moreover the performances of diversely skilled agents will be extensively tested and evaluated showing the robustness of the approach.
Evolution of Computer Vision Subsystems in Robot Navigation and Image Classification Tasks

Real-time decision making based on visual sensory information is a demanding task for mobile robots. Learning on high-dimensional, highly redundant image data imposes a real problem for most learning algorithms, especially those being based on neural networks. In this paper we investigate the utilization of evolutionary techniques in combination with supervised learning of feedforward nets to automatically construct and improve suitable, task-dependent preprocessing layers helping to reduce the complexity of the original learning problem. Given a number of basic, parameterized low-level computer vision algorithms, the proposed evolutionary algorithm automatically selects and appropriately sets up the parameters of exactly those operators best suited for the imposed supervised learning problem.
RoboCup 2003 : New Scientific and Technical Advancements

This paper reports on the RoboCup-2003 event. RoboCup is no longer just the Soccer World Cup for autonomous robots, but has evolved to become a coordinated initiative encompassing four different robotics events: Soccer, Rescue, Junior (focused on education) and a Scientific Symposium. RoboCup-2003 took place from the 2nd to the 11th of July 2003 in Padua (Italy), and it was co-located with other scientific events in the field of AI and Robotics. In this paper, in addition to reporting on the results of the games, we highlight the Robotics and AI technologies exploited by the teams in the different leagues and describe the most meaningful scientific contributions.
Fynesse: An architecture for integrating prior knowledge in autonomously learning agents

Reinforcement learning is an optimisation technique for applications like control or scheduling problems. It is used in learning situations, where success and failure of the system are the only training information. Unfortunately, we have to pay a price for this powerful ability: long training times and the instability of the learning process are not tolerable for industrial applications with large continuous state spaces. From our point of view, the integration of prior knowledge is a key mechanism for making autonomous learning practicable for industrial applications. The learning control architecture Fynesse provides a unified view onto the integration of prior control knowledge in the reinforcement learning framework. In this way, other approaches in this area can be embedded into Fynesse. The key features of Fynesse are (1) the integration of prior control knowledge like linear controllers, control characteristics or fuzzy controllers, (2) autonomous learning of control strategies and (3) the interpretation of learned strategies in terms of fuzzy control rules. The benefits and problems of different methods for the integration of a priori knowledge are demonstrated on empirical studies.
Brainstormers 2004-Team Description

The main interest behind the Brainstormers’ effort in the robocup soccer domain is to develop and to apply machine learning techniques in complex domains. Especially, we are interested in reinforcement learning methods, where the training signal is only given in terms of success or failure. Our final goal is a learning system, where we only plug in ’win the match’ and our agents learn to generate the appropriate behaviour. Unfortunately, even from very optimistic complexity estimations it becomes obvious, that in the soccer domain, both conventional solution techniques and also advanced today’s reinforcement learning techniques come to their limit there are more than (108×50)23 different states and more than (1000) different policies per agent per half time. This paper describes the new architecture of the Brainstormers team, the improved self-localization using particle filters and the extensions of the learning algorithm to simultaniously learn with and without ball behaviors.
Line Based Robot Localization under Natural Light Conditions

In this paper we present a framework for robot selflocalization in the robocup middle size league. This framework comprises an algorithm for robust selflocalization and a set of benchmarks which can be used offline to test other algorithms and to compare their outcomes with our results. The algorithm is part of our competition team BrainstormersTribots which won the Robocup German Open 2004. This is a multi agent real time environment, therefore our algorithm is prepared to work with 30 frames per second, leaving enough time for other tasks like robot control or path planning. Our approach uses a particle filter method relying on features found in the image. The features are points on field lines. They can be recognized reliably under natural light conditions, so the is no longer a need for a well defined and constant light source. Also color coded landmarks or goals are not required for a stable selflocalization. We present results for different runs on our benchmark suite, which is an outdoor soccer field with the size of 16x10m. This field size is bigger then in current competitions and anticipates the trend of using larger fields in future competitions.
RoboCup-2003: New Scientific and Technical Advances

This article reports on the RoboCup-2003 event. RoboCup is no longer just the Soccer World Cup for autonomous robots but has evolved to become a coordinated initiative encompassing four different robotics events: (1) Soccer, (2) Rescue, (3)Junior (focused on education), and (4) a Scientific Symposium. RoboCup-2003 took place from 2 to 11 July 2003 in Padua (Italy); it was colocated with other scientific events in the field of AI and robotics. In this article, in addition to reporting on the results of the games, we highlight the robotics and AI technologies exploited by the teams in the different leagues and describe the most meaningful scientific contributions.
RoboCup: Yesterday, Today, and Tomorrow Workshop of the Executive Committee in Blaubeuren, October 2003

has been known for the goal of “creating robots capable of beating the world cup in 2050.” Clearly, we stated this goal back in 1996 not as an exact scientific goal, but as an audacious challenge to pursue. We aimed at creating a far away target for RoboCup researchers, as we were well aware that the development of fully autonomous soccer robots capable of competing against human world champion soccer players was a rather long term research project.
Overview of RoboCup 2003 Competition and Conferences

2003, the seventh RoboCup Competition and Conference, took place between July the 2nd and July the 11th 2003 in Padua (Italy). The teams had three full days to setup their robots. The competitions were held in the new pavilion no7 of the Fair of Padua . Several scientific events in the field of Robotics and Artificial Intelligence were held in parallel to the competitions. The RoboCup Symposium was held in the last two days. The opening talks took place in the historical Main Hall of the University of Padua and the three parallel Symposium sections in the conference rooms of the Fair of Padua.