MULTILEVEL SYNTHESIS OF FINITE STATE MACHINES BASED ON SYMBOLIC FUNCTIONAL DECOMPOSITION

2006 ◽  
Vol 06 (02) ◽  
pp. 257-271 ◽  
Author(s):  
MARIUSZ RAWSKI ◽  
HENRY SELVARAJ ◽  
TADEUSZ ŁUBA ◽  
PIOTR SZOTKOWSKI
Keyword(s):  
Internal State ◽  
Logic Synthesis ◽  
Optimal Solution ◽  
Good Method ◽  
Functional Decomposition ◽  
Novel Approach ◽  
Target Architecture ◽  
Finite State ◽  
Field Programmable ◽  
State Encoding

This paper presents a Finite State Machine (FSM) implementation method based on symbolic functional decomposition. This novel approach to multilevel logic synthesis of FSMs targets Field Programmable Gate Array (FPGA) architectures. Traditional methods consist of two steps: internal state encoding and then mapping the encoded state transition table into target architecture. In the case of FPGAs, functional decomposition is recognized as the most efficient method of implementing digital circuits. However, none of the known state encoding algorithms can be considered as a good method to be used with functional decomposition. In this paper, the concept of symbolic functional decomposition is applied to obtain a multilevel structure that is suitable for implementation in FPGA architectures. The symbolic functional decomposition does not require a separate encoding step. It accepts FSM description with symbolic states and performs decomposition, producing such a state encoding that guarantees the optimal or near-optimal solution.

scholarly journals A Novel Approach for Solving Nonsmooth Optimization Problems with Application to Nonsmooth Equations

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Hamid Reza Erfanian ◽  
M. H. Noori Skandari ◽  
A. V. Kamyad
Keyword(s):  
Nonsmooth Optimization ◽  
Optimization Problems ◽  
Piecewise Linear ◽  
Linear Optimization ◽  
Optimal Solution ◽  
Piecewise Linear Function ◽  
Nonsmooth Equations ◽  
Smooth Functions ◽  
Generalized Derivative ◽  
Novel Approach

We present a new approach for solving nonsmooth optimization problems and a system of nonsmooth equations which is based on generalized derivative. For this purpose, we introduce the first order of generalized Taylor expansion of nonsmooth functions and replace it with smooth functions. In other words, nonsmooth function is approximated by a piecewise linear function based on generalized derivative. In the next step, we solve smooth linear optimization problem whose optimal solution is an approximate solution of main problem. Then, we apply the results for solving system of nonsmooth equations. Finally, for efficiency of our approach some numerical examples have been presented.

Automatic Extraction of Semantic Relations from Wikipedia

2015 ◽  
Vol 24 (02) ◽  
pp. 1540010 ◽  
Author(s):  
Patrick Arnold ◽  
Erhard Rahm
Keyword(s):  
Finite State Machines ◽  
Background Knowledge ◽  
Semantic Relations ◽  
Automatic Extraction ◽  
State Machines ◽  
High Quality ◽  
Novel Approach ◽  
Finite State ◽  
Semantic Ontology

We introduce a novel approach to extract semantic relations (e.g., is-a and part-of relations) from Wikipedia articles. These relations are used to build up a large and up-to-date thesaurus providing background knowledge for tasks such as determining semantic ontology mappings. Our automatic approach uses a comprehensive set of semantic patterns, finite state machines and NLP techniques to extract millions of relations between concepts. An evaluation for different domains shows the high quality and effectiveness of the proposed approach. We also illustrate the value of the newly found relations for improving existing ontology mappings.

A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

2021 ◽  
Vol 1 (2) ◽  
pp. 1-23
Author(s):  
Arkadiy Dushatskiy ◽  
Tanja Alderliesten ◽  
Peter A. N. Bosman
Keyword(s):  
Boolean Functions ◽  
Surrogate Model ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Optimal Solution ◽  
Problem Structure ◽  
Novel Approach ◽  
Efficient Learning ◽  
Nk Landscapes

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the domain of pseudo-Boolean functions in a black-box setting. Moreover, instead of using a surrogate model as an approximation of a fitness function, we propose to precisely learn the coefficients of the Walsh decomposition of a fitness function and use the Walsh decomposition as a surrogate. If the coefficients are learned correctly, then the Walsh decomposition values perfectly match with the fitness function, and, thus, the optimal solution to the problem can be found by optimizing the surrogate without any additional evaluations of the original fitness function. It is known that the Walsh coefficients can be efficiently learned for pseudo-Boolean functions with k -bounded epistasis and known problem structure. We propose to learn dependencies between variables first and, therefore, substantially reduce the number of Walsh coefficients to be calculated. After the accurate Walsh decomposition is obtained, the surrogate model is optimized using GOMEA, which is considered to be a state-of-the-art binary optimization algorithm. We compare the proposed approach with standard GOMEA and two other Walsh decomposition-based algorithms. The benchmark functions in the experiments are well-known trap functions, NK-landscapes, MaxCut, and MAX3SAT problems. The experimental results demonstrate that the proposed approach is scalable at the supposed complexity of O (ℓ log ℓ) function evaluations when the number of subfunctions is O (ℓ) and all subfunctions are k -bounded, outperforming all considered algorithms.

scholarly journals A Novel Computational Approach for Harmonic Mitigation in PV Systems with Single-Phase Five-Level CHBMI

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2100 ◽  
Author(s):  
Rosario Miceli ◽  
Giuseppe Schettino ◽  
Fabio Viola
Keyword(s):  
Single Phase ◽  
Switching Frequency ◽  
Polynomial Equations ◽  
Voltage Waveform ◽  
Pv Systems ◽  
Time Operation ◽  
Novel Approach ◽  
Field Programmable ◽  
Real Time Operation ◽  
Harmonic Mitigation

In this paper, a novel approach to low order harmonic mitigation in fundamental switching frequency modulation is proposed for high power photovoltaic (PV) applications, without trying to solve the cumbersome non-linear transcendental equations. The proposed method allows for mitigation of the first-five harmonics (third, fifth, seventh, ninth, and eleventh harmonics), to reduce the complexity of the required procedure and to allocate few computational resource in the Field Programmable Gate Array (FPGA) based control board. Therefore, the voltage waveform taken into account is different respect traditional voltage waveform. The same concept, known as “voltage cancelation”, used for single-phase cascaded H-bridge inverters, has been applied at a single-phase five-level cascaded H-bridge multilevel inverter (CHBMI). Through a very basic methodology, the polynomial equations that drive the control angles were detected for a single-phase five-level CHBMI. The acquired polynomial equations were implemented in a digital system to real-time operation. The paper presents the preliminary analysis in simulation environment and its experimental validation.

scholarly journals Novel Approach for Modeling Wireless Fading Channels Using a Finite State Markov Chain

ETRI Journal ◽  
2017 ◽  
Vol 39 (5) ◽  
pp. 718-728 ◽  
Author(s):  
Ahmed Abdul Salam ◽  
Ray Sheriff ◽  
Saleh Al-Araji ◽  
Kahtan Mezher ◽  
Qassim Nasir
Keyword(s):  
Markov Chain ◽  
Fading Channels ◽  
Novel Approach ◽  
Finite State

Exploiting Energy–Quality (E–Q) Tradeoffs: A Case Study on Color-to-Grayscale Converters with Approximate Design on FPGA

2020 ◽  
pp. 2150062
Author(s):  
Xiaokun Yang ◽  
Shi Sha
Keyword(s):  
Video Processing ◽  
Energy Savings ◽  
Experimental Result ◽  
Approximate Design ◽  
Diverse Range ◽  
Output Quality ◽  
Finite State ◽  
Field Programmable

Today, field programmable gate array (FPGA) is becoming widely used as computational accelerators in many application domains such as image/video processing, machine learning, and data mining. The inherent tolerance to the imprecise computation in such domains potentially provides an opportunity to trade quality of the results for higher energy efficiency. Therefore, this paper proposes a systematic methodology aiming to find the optimal energy saving corresponding to different quality bound, by approximating register-transfer level (RTL) designs on FPGA. As a case study, first, we investigate imprecise design on two submodules — adders and multipliers. By integrating the two combinational submodules with finite state machines (FSMs), several designs on a sequential circuit — color-to-grayscale converter — are further presented to offer a diverse range of energy consumption related to different quality constrains. Through this, we are able to set energy–quality (E–Q) parameters of our proposed methodology and configure the approximation knobs, capable of maximizing energy savings within different application-based quality margins. Experimental result demonstrates that leveraging E–Q leads to an average [Formula: see text]–[Formula: see text] savings in energy for modest loss in application output quality ([Formula: see text]), and [Formula: see text]–[Formula: see text] energy savings for impact on relaxed quality constraints (3–7.5%).

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