Filipe R. Cordeiro

Heuristic for two-level cache hierarchy exploration considering energy consumption and performance

In this work is presented an automated method for adjusting two-level cache memory hierarchy in order to reduce energy consumption in embedded applications. The proposed heuristic, TECH-CYCLES (Two-level Cache Exploration Heuristicconsidering CYCLES), consists of making a small search in the space of configurations of the two-level cache hierarchy, analyzing the impact of each parameter in terms of energy and number of cycles spent for a given application. Experiments show an average reduction of about 41% in the energy consumption by using our heuristic when compared with the existing heuristic (TCaT), also for two-level caches. Besides the energy improvement, this method also reduces the number of cycles needed to execute a given application by about 25%. In order to validate the proposed heuristic, twelve benchmarks from the MiBench suite have been used.

An Intelligent Mechanism to Explore a Two-Level Cache Hierarchy Considering Energy Consumption and Time Performance

Cache memory hierarchy contributes positively to system performance. Moreover, tuning cache architectures in platforms for embedded applications can dramatically reduce energy consumption. This paper presents an automated method for adjusting two-level cache memory hierarchy intended for data caches in order to reduce energy consumption and improve the performance of embedded applications. We propose an automated mechanism called TEMGA (Two-level cache Exploration Mechanism based on Genetic Algorithm), to determine the suitable cache hierarchy configuration by exploring a small part of search space. In our experiments, we applied the proposed mechanism to 12 different benchmarks from the MiBench suite. The results show an average reduction of about 15% in the energy consumption for data caches when compared to existing heuristics and a reduction of 5 times in the number of cycles needed to execute applications from Mibench Benchmark suite.Speedup in distributed executions of constraint logic programming (CLP) applications are directed related to a good constraint partitioning algorithm. In this work we study different mechanisms to distribute constraints to processors based on straightforward mechanisms such as round-robin and block distribution, and on a more sophisticated automatic distribution method, grouping-sink, that takes into account the connectivity of the constraint network graph. This aims at reducing the communication overhead in distributed environments. Our results show that grouping-sink is, in general, the best alternative for partitioning constraints as it produces results as good or better than round-robin or blocks with low communication rate.

An ESL approach for energy consumption analysis of cache memories in SoC platforms

The design of complex circuits as SoCs presents two great challenges to designers. One is the speeding up of system functionality modeling and the second is the implementation of the system in an architecture that meets performance and power consumption requirements. Thus, developing new high-level specification mechanisms for the reduction of the design effort with automatic architecture exploration is a necessity. This paper proposes an Electronic-System-Level (ESL) approach for system modeling and cache energy consumption analysis of SoCs called PCacheEnergy Analyzer. It uses as entry a high-level UML-2.0 profile model of the system and it generates a simulation model of a multicore platform that can be analyzed for cache tuning. PCacheEnergyAnalyzer performs static/dynamic energy consumption analysis of caches on platforms that may have different processors. Architecture exploration is achieved by letting designers choose different processors for platform generation and different mechanisms for cache optimization. PCacheEnergy Analyzer has been validated with several applications of Mibench, Mediabench, and PowerStone benchmarks, and results show that it provides analysis with reduced simulation effort

An Optimization Mechanism Intended for Two-Level Cache Hierarchy to Improve Energy and Performance Using the NSGAII Algorithm

Tuning cache architectures in MPSoC platforms for embedded applications can dramatically reduce energy consumption. This paper presents a design tool for adjusting a two-level cache memory hierarchy that uses a fast non-dominated sorting algorithm (NSGAII) in order to provide decision support capabilities. It aims to reduce energy consumption and improve the performance of embedded applications. This optimization mechanism finds the best set of cache configurations (Pareto-Front) and offers support to the architecture designer in order to provide a set of non-dominated solutions for a decision maker. In our experiments, we applied the proposed mechanism to 12 different applications from the MiBench benchmark suite. Furthermore, the simulation results showed that the solutions found by our proposal are comparable to the results of other techniques and, for 67% of the analyzed cases, the efficiency of the mechanism was achieved.

An adaptive semi-supervised Fuzzy GrowCut algorithm to segment masses of regions of interest of mammographic images

Graphical abstractDisplay Omitted According to the World Health Organization, breast cancer is the most common cancer in women worldwide, becoming one of the most fatal types of cancer. Mammography image analysis is still the most effective imaging technology for breast cancer diagnosis, which is based on texture and shape analysis of mammary lesions. The GrowCut algorithm is a general-purpose segmentation method based on cellular automata, able to perform relatively accurate segmentation through the adequate selection of internal and external seed points. In this work we propose an adaptive semi-supervised version of the GrowCut algorithm, based on the modification of the automaton evolution rule by adding a Gaussian fuzzy membership function in order to model non-defined borders. In our proposal, manual selection of seed points of the suspicious lesion is changed by a semiautomatic stage, where just the internal points are selected by using a differential evolution algorithm. We evaluated our proposal using 57 lesion images obtained from MiniMIAS database. Results were compared with the semi-supervised state-of-the-art approaches BEMD, BMCS, Wavelet Analysis, LBI, Topographic Approach and MCW. Results show that our method achieves better results for circumscribed, spiculated lesions and ill-defined lesions, considering the similarity between segmentation results and ground-truth images.

A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic images

We propose a Fuzzy semi-supervised version of the GrowCut algorithm.We reduced dependence of GrowCut on user experience, using simulated annealing.To improve robustness to point selection, we modified the GrowCut evolution rule.We evaluated our approach by classifying 685 digital mammograms.Our approach could reach an overall accuracy of 91% for fat tissues. According to the World Health Organization, breast cancer is the most common form of cancer in women. It is the second leading cause of death among women round the world, becoming the most fatal form of cancer. Despite the existence of several imaging techniques useful to aid at the diagnosis of breast cancer, x-ray mammography is still the most used and effective imaging technology. Consequently, mammographic image segmentation is a fundamental task to support image analysis and diagnosis, taking into account shape analysis of mammary lesions and their borders. However, mammogram segmentation is a very hard process, once it is highly dependent on the types of mammary tissues. The GrowCut algorithm is a relatively new method to perform general image segmentation based on the selection of just a few points inside and outside the region of interest, reaching good results at difficult segmentation cases when these points are correctly selected. In this work we present a new semi-supervised segmentation algorithm based on the modification of the GrowCut algorithm to perform automatic mammographic image segmentation once a region of interest is selected by a specialist. In our proposal, we used fuzzy Gaussian membership functions to modify the evolution rule of the original GrowCut algorithm, in order to estimate the uncertainty of a pixel being object or background. The main impact of the proposed method is the significant reduction of expert effort in the initialization of seed points of GrowCut to perform accurate segmentation, once it removes the need of selection of background seeds. Furthermore, the proposed method is robust to wrong seed positioning and can be extended to other seed based techniques. These characteristics have impact on expert and intelligent systems, once it helps to develop a segmentation method with lower required specialist knowledge, being robust and as efficient as state of the art techniques. We also constructed an automatic point selection process based on the simulated annealing optimization method, avoiding the need of human intervention. The proposed approach was qualitatively compared with other state-of-the-art segmentation techniques, considering the shape of segmented regions. In order to validate our proposal, we built an image classifier using a classical multilayer perceptron. We used Zernike moments to extract segmented image features. This analysis employed 685 mammograms from IRMA breast cancer database, using fat and fibroid tissues. Results show that the proposed technique could achieve a classification rate of 91.28% for fat tissues, evidencing the feasibility of our approach.

A combined optimization method for tuning two-level memory hierarchy considering energy consumption

Tuning cache hierarchies in platforms for embedded systems can significantly reduce energy consumption. In this paper we combined two optimization methods for tuning both instruction and data cache configurations in a two-level memory hierarchy, where both levels have separate instruction and data caches. This kind of hierarchy allows us to evaluate instruction and data caches branches separately, although previous approaches have applied the same method for both branches of the hierarchy. This work evaluates several methods intended for two-level hierarchies, and the results showed that when we combine different methods for each branch of the hierarchy, results can be improved. Experiments based on simulations were performed for 12 applications from the Mibench suite benchmark and the combined method achieved better efficiency in 60% of the evaluated cases compared with existing heuristics. The proposed solution is only 11% less economic in terms of energy consumption than optimal values and required, on average, 42 simulations to conclude optimization mechanism, representing only 9% of the design space.

MOPSO Applied to Architecture Tuning with Unified Second-Level Cache for Energy and Performance Optimization

Design Space Exploration (DSE) have been a suitable strategy to configure a parameterized SoC platform in terms of systems requirements such as energy and performance. In this work, a multi-objective approach (MOPSO) based on Particle Swarm Optimization was applied for DSE problems for supporting architecture tuning in memory hierarchy with unified second level cache. The proposed approach considers two objectives to be optimized: energy consumption and application performance; and allows to reduce the design space by exploring only 2,64% of the exploration space. Results of MOPSO with regard to cost function found solutions approaching Pareto Optimum in terms of energy consumption and performance in the majority of cases, about 66% of the studied cases. Experiments based on simulations were carried out on 18 applications from the Mibench and PowerStone suite benchmarks.

An environment for energy consumption analysis of cache memories in SoC platforms

The tuning of cache architectures in platforms for embedded systems applications can dramatically reduce energy consumption. The existing cache exploration environments constrain the designer to analyze cache energy consumption on single processor systems and worse, systems that are based on a single processor type. In this paper is presented the PCacheEnergyAnalyzer environment for energy consumption analysis of cache memory on SoC platforms. This is a powerful energy analysis environment that combines the use of efficient tools to provide static and dynamic energy consumption analysis, the flexibility to support the architecture exploration of cache memories on platforms that are not bound to a specific processor, and fast simulation techniques. The proposed environment has been integrated into the SoC modeling framework PDesigner, providing a user-friendly graphical interface allowing the integrated modeling and cache energy analysis of SoCs. The PCacheEnergyAnalyzer has been validated with four applications of the Mediabench suite benchmark.

Automatic Segmentation of Melanoma in Dermoscopy Images Using Fuzzy Numbers

Melanoma is the most dangerous type of skin cancer, but when treated in its early stages the chance of cure is increased. However, the detection of melanoma is a challenging task even for specialists due to low contrast of skin lesions and presence of artifacts. Therefore, developing an automatic segmentation tool for skin lesion analysis using dermoscopy images is a critical step for improving the diagnosis. This work proposes an automatic melanoma segmentation approach based on Fuzzy Numbers. The technique was evaluated using 571 images from ISDI data set, composed of 446 benign lesions and 125 malignant melanoma. The proposed approach was compared with three state-of-art techniques and was evaluated through the metrics of sensitivity, specificity, Jaccard index, and balanced accuracy. Results show that the fuzzy approach has a better segmentation compared to the other techniques, obtaining values of 0.77 of sensibility, 0.94 of specificity, 0.65 of the Jaccard index, and 0.85 of balanced accuracy. Results demonstrate that the segmentation approach using fuzzy numbers is highly competitive with all algorithms analyzed.