Evolvable sonar spectrum discrimination chip designed by genetic algorithm

Cache memories have been extensively used to bridge the gap between high speed processors and relatively slower main memories. However, they are sources of predictability problems because of their dynamic and adaptive behavior, and thus need special attention to be used in hard real-time systems. A lot of progress has been achieved in the last ten years to statically predict worst-case execution times (WCETs) of tasks on architectures with caches. However, cache-aware WCET analysis techniques are not always applicable or may be too pessimistic. An alternative approach allowing to use caches in real-time systems is to lock their contents (i.e. disable cache replacement) such that memory access times and cache-related preemption times are predictable. In this paper, we compare the performance of two algorithms for static locking of instruction caches: one using a genetic algorithm for cache contents selection (A.M. Campoy et al., 2001) and a pragmatical algorithm, called her-after reference-based algorithm (I. Puaut and D. Decotigny), which uses the string of memory references issued by a task on its worst-case execution path as an input of the cache contents selection algorithm. Experimental results show that (i) both algorithms behave identically with respect to the system worst-case utilization; (ii) the genetic algorithm behaves slightly better than the reference-based algorithm with respect to the average slack of tasks; (iii) the execution time of the cache-contents selection procedure is much better when using the reference-based algorithm than with the genetic algorithm.

In this work, we propose a novel technique for evolving transistor netlists from truth table descriptions of arbitrary digital circuits. The proposed methods incorporate the effective use of genetic algorithms (GAs). In typical semi-custom and custom design flows, logic optimization is done at the gate level after Boolean translation of the input truth table. The final transistor netlist is then deduced from the simplified gate logic to be laid out on a chip. However transistor level optimizations after the Boolean simplification step would still not lead to the minimum number of transistors. This final optimization level is non-existent in present custom design flows. This work aims to address this need. A salient feature of the proposed technique is the bypassing of gate level representation and optimization in the VLSI design flow. We provide genetic methods to directly optimize truth table inputs using transistor level simplification. This eliminates the intermediate gate level optimization step and provides optimized transistor netlists which could be used for dynamic library cell generation for custom and semi-custom designs on the fly.

The IBM Corporation's Federal Systems Division (FSD) has designed, developed and implemented a VLSI design methodology selected specifically for the DoD's design environment. The military industry VLSI requirements and IBM FSD's design system goals for developing chip design methodology are described. Substantial reduction in computer costs and design schedule using the methodology and the design system components is demonstrated. The rationale behind selecting the specific combination of tools, from the many design systems in IBM, for forming the FSD chip design system and the methodology is explained.

In a VLSI design environment where a range of chip technologies are available and concurrent chip designs are commonplace, it is not feasible to build a wiring program for each technology. Additionally, a chip's design methodology may demand specific abilities from a wiring program. KWIRE was developed to meet the needs of a multiple-technology, multiple-methodology VLSI design community. It has been used on a range of chips, from small designs to custom microprocessors. Modeling the users' designs and design rules in geometric terms allows KWIRE to handle such a diversity of chip designs. This paper describes the KWIRE system and router.

Realising radar high-speed real-time signal processing under the space-borne environment is a worldwide research difficult point. Commercial Quartus devices required for high-speed processing are hardened and consolidated in high radiation environments. This study realises high-speed real-time radar signal processing in space-borne environment for doing the reinforced design of commercial Quartus chip for pulse compression algorithm. The article first analysed the failure modes of high-performance FPGAs in a space-borne environment in China: a highly efficient parallel pipeline architecture with 8-8 K points is designed; control registers, state machine registers, on-chip RAM, and other easy-to-flip positions have been fully reinforced for the pulse compression algorithm. The system realises 200 M real-time processing capability after integration. Single-event fault injection tests were performed on key signals such as system control, and the test results verified the effectiveness of the reinforcement. The fault tolerance of the system has been significantly improved to ensure the normal operation of the FPGA.

This research aims at developing adaptive medical diagnostic system driven by genetic, neural network and fuzzy logic algorithms for effective diagnosis of tuberculosis. Medical diagnosis for tuberculosis disease is a complex decision process that involves a lot of vagueness and uncertainty management, since the disease has multiple symptoms. The use of several algorithms has been explored in clinical diagnosis models for diagnosing and prescribing therapy for tuberculosis, but has challenges in transforming the vagueness and uncertainty of multiple symptoms due to the noisy nature of the disease data and over fitting of the models, this had led to the challenge of accurate classification, training, optimization, diagnosis and prescription of therapy. Object Oriented Analysis and Design (OOAD) and System Structured Analysis and Design (SSAD) methodologies were adopted in the research. The methodology involved obtaining four hundred and thirty (430) clinical data from patients with tuberculosis records at the Tuberculosis and Leprosy Hospital, Eku, Delta State. The obtained data were pre-processed using missing values imputation and numeric data encoding methods. Thereafter, a genetic algorithm was applied to the processed data to select six relevant features from the initial number of 16 features (Cough, Night sweats, Fever, Systolic Blood Pressure, Difficulty in Breathing, Loss of appetite, Sputum, Chills, Loss of pleasure, Immune Suppression, Chest Pain, Lack of concentration, Irritation, Loss of energy, Lymph Node Enlargement, Body Mass Index). The reduced dataset was split into training and test sets. The ANFIS (Adaptive Neuro-Fuzzy Inference System) was applied to perform diagnosis in which it was trained and validated on training and test sets respectively. The ANFIS was driven by Mamdani’s inference mechanism with sixty-four (64) generated rules with confidence and support scores of above 10% and 15% respectively. The developed model performance on the test sets gave an accuracy of 90% precision, sensitivity, and specificity of 100%. The results showed that all the attributes of tuberculosis contributed to the degree of the diseases based on their respective weights. Conclusively, the system accurately classified, trained attributes, and predicts the severity of tuberculosis.

As the technology node scales down below nanometer, the design rules significantly increase, which in turn results in the huge search problems of the chip design with an early-stage technology. DTCO (Design-Technology Co-Optimization) becomes a key methodology in semiconductor industry to overcome the limitation of physical scaling and transistor density, and to improve performance and power efficiency [1-2]. However, DTCO induces huge increase of design spaces in physical implementation since chip designers should tune the technology related variables along with the conventional design variables at the early technology stage. ML (machine learning) is a powerful technique when tuning and optimizing such complicated parameters in technology and design [3]. Synopsys DSO.ai (Design Space Optimization AI) is an early innovator of ML (Machine Learning) in EDA (Electronic Design Automation) area, where the tool uses advancements of RL (Reinforcement Learning) to effectively search the massive design space for global near-optimal targets [4]. In this paper, we report DSO.ai applications on 3nm technology and designs, such as PPA (Performance-Power-Area) improvements in both HP (high performance) and HD (high density) libraries, NDR (non-default rule) routing on both clock and signal nets, LP (layer promotion), density control, routing wire and via cost tuning, and more. Furthermore, we utilize ML capability in searching and optimizing technology parameters, e.g., key design pitches and design rules, as well as design parameters, e.g., via/wire cost, placeholder densities, legalization and routing strategies, and other EDA settings, to maximize PPA metric and to minimize DRC violation. Our goal is to find the best values of the initial parameters from an early technology and design stages and to encapsulate them in RM (Reference Methodology) script to provide the designer with the mega switch in entire design flows. Parameter range (to be explored) and evaluation metrics (to score QOR) are defined, then DSO.ai explores search spaces and trains itself to minimize the optimization cost. Optimized parameter values are evaluated against the reference parameters to reproduce the benefit across various designs and technology collaterals. In this manner, different parameters can be optimized given design methodology. This iterative process can be extended to optimize all design technology parameters to maximize PPA on 3nm design technology and designs and beyond. Proposed approach applied to optimize achievable core area and improve max frequency, respectively. Design flow with ML-assisted parameter could reproduce improvement across the different size and different styles of the benchmark designs. Optimization for achievable core area could reduce -0.5% at cell-driven design and -0.6% at route-driven design. Optimization for performance could improve the frequency of +1.42% at cell-driven design and +1.43% at route-driven design in Samsung 3nm technology.

Multiobjective Optimum Design of Structures with Genetic Algorithm and Game Theory: Application to Life-Cycle Cost Design

This work addresses the indirect heat integration (i.e. resorting to intermediate heat storage) and the direct heat integration (i.e. heat exchanges between coexisting process streams) of batch processes. Tools and methods for the targeting of these two limiting cases of heat integration are proposed, and completed by the development and the application of an automatic design & optimization methodology using the Struggle genetic algorithm (GA). A brewery process is used to demonstrate the feasibility and the practical relevance of the proposed indirect heat integration models. The fluctuations of the process schedule and their effects on the optimal solutions are not modelled (the indirect heat integration is known to feature an inherently low sensitivity). The rescheduling opportunities are not searched for. For the indirect heat integration, fixed temperature/variable mass inventory heat storage units (HSUs) are applied. Two models of indirect heat recovery schemes (IHRSs) are proposed : one based on a closed heat storage system, another built around an open storage system suitable e.g. for food and beverages industries. The inequality constraints of the IHRS models are automatically met owing to an appropriate definition of the decision variables managed by the GA. A rough preadjustment of the mass balance equality constraints on HSUs is achieved by a preliminary stage of heat recovery (HR) maximization before actually minimizing the total batch costs (TBCs). Optimization runs and theoretical considerations on the generation & replacement strategy of Struggle demonstrate that the structural and the parametric variables cannot be efficiently optimized within a single level, resulting in a two-levels optimization scheme. The automatically designed closed storage IHRS solutions for the brewing process are as good as the solutions obtained by another author using a combinatorial method followed by a post-optimization stage. The open storage IHRS is 13 % cheaper while the HR increases by 12 %. Optimizing the IHRS on a one-week period (including the non-periodic start-up & shut-down phases) results in an even more realistic solution, featuring a significantly different trade-off between energy, HSU capacities and HEX areas. A GA based, two-levels optimization scheme is proposed for the design of direct batch heat exchanger networks (HENs). The HEN structures, managed by the upper-level GA, do not include stream splitting. The re-use of HEX units across time slices is a key issue and a methodology to specify the actually possible structural changes by repipe or resequence is proposed, accounting for the thermo-physical compatibility, chemical compatibility, and process schedule constraints. The optimum operation of an existing HEN during each time slice has been analysed and a sound solution procedure is proposed.

A methodology for the preliminary design and performance prediction of high-pressure ratio radial-inflow turbines

The design of finite impulse response (FIR) filters can be formulated as a non‐linear optimization problem reputed to be difficult for conventional approaches. The constraints are high and a large number of parameters have to be estimated, especially when dealing with two‐dimensional FIR filters. In order to improve the performance of conventional approaches, the authors explore several stochastic methodologies capable of handling large spaces. The authors specifically propose a new genetic algorithm (GA) in which some innovative concepts are introduced to improve the convergence and make its use easier for practitioners. The algorithm is globally improved by adapting the mutation and crossover and selection operators with the genetic advances. A dynamic ranking selection scheme is introduced to limit the promotion of extraordinary chromosomes. A refreshing mechanism is investigated to manage the trade‐off between diversity and elitism. The key point of the proposed approach stems from the capacity of the GA to adapt the genetic operators during the genetic life while remaining simple and easy to implement. Most of the parameters and operators are changed by the GA itself. From an initial calibration, the GA performs the design problem while calibrating and repeatedly re‐calibrating itself for solving it. The authors demonstrate on various cases of filter design a significant improvement in performance.

The aim of this paper is to introduce generalized design and optimization methodology for LLC resonant converters. To investigate operation principles, deriving linear circuit and mathematical models, first harmonic approximation is used which to great extent can simplifies quantitative analysis. For optimal, high efficiency design of LLC converter with controllability feature over wide variation of input voltage, mathematical model of gain function and input impedance are reordered to be used as fitness function and constrain functions and 'Genetic Algorithm' is used to achieve best possible combination of resonant tank elements. Optimized values of Qe, Ln and n is selected and simulation is carried out using PSIM software to verify the accuracy of the proposed method.

Purpose – The purpose of this paper is to develop a novel technique for the pre-design of a printed circuit board (PCB) of a DC-DC power converters where the placement of electric components can cancel the electromagnetic emissions through subtractive coupling and in this sense to minimize the stray magnetic and electric fields at a specific location. For this work the location of interest is a current transducer used for control purposes positioned in the center of a DC-DC Cuk converter board as a constrain. Design/methodology/approach – The methodology of design is based on the development of an interface software platform through MatLab script coding which interconnects the solution of a numerical analysis software and an optimization technique. The numerical analysis software is based on finite element calculations where quasi-static field analysis are performed to calculate the radiated electric and magnetic fields. The optimization technique is conducted by genetic algorithms (GAs). Findings – The r...

The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected.

A methodology is proposed for designing a low-energy consuming ternary-valued full adder based on a quantum dot (QD) electrostatically coupled with a single electron transistor operating as a charge sensor. The methodology is based on design optimization: the values of the physical parameters of the system required for implementing the logic operations are optimized using a multiobjective genetic algorithm. The searching space is determined by elements of the capacitance matrix describing the electrostatic couplings in the entire device. The objective functions are defined as the maximal absolute error over actual device logic outputs relative to the ideal truth tables for the sum and the carry-out in base 3. The logic units are implemented on the same device: a single dual-gate quantum dot and a charge sensor. Their physical parameters are optimized to compute either the sum or the carry out outputs and are compatible with current experimental capabilities. The outputs are encoded in the value of the electric current passing through the charge sensor, while the logic inputs are supplied by the voltage levels on the two gate electrodes attached to the QD. The complex logic ternary operations are directly implemented on an extremely simple device, characterized by small sizes and low-energy consumption compared to devices based on switching single-electron transistors. The design methodology is general and provides a rational approach for realizing non-switching logic operations on QD devices.