Extending ATL for Native UML Profile Support: An Experience Report 49-62

International audienceWith the rise of Model-driven Engineering (MDE) the ap- plication field of model transformations broadens drastically. Current model transformation languages provide appropriate support for stan- dard MDE scenarios such as model-to-model transformations specified between metamodels. However, for other transformation scenarios often the escape to predefined APIs for handling specific model manipulations is required such as is the case for supporting UML profiles in transforma- tions. Thus, the need arises to extend current transformation languages for natively supporting such additional model manipulations. In this paper we report on extending ATL for natively supporting UML profiles in transformations. The extension is realized by providing an extended ATL syntax comprising keywords for handling UML profiles which is reduced by a preprocessor based on a Higher-Order Transfor- mation (HOT) again to the standard ATL syntax. In particular, we elab- orate on our methodology of extending ATL by presenting the extension process step-by-step as well as reporting on lessons learned. With this experience report we aim at providing design guidelines for extending ATL as well as stimulating the research of providing further extensions for ATL

Configurable Software Performance Completions through Higher-Order Model Transformations

Chillies is a novel approach for variable model transformations closing the gap between abstract architecture models, used for performance prediction, and required low-level details. We enable variability of transformations using chain of generators based on the Higher-Order Transformation (HOT). HOTs target different goals, such as template instantiation or transformation composition. In addition, we discuss state-dependent behavior in prediction models and quality of model transformations

Configurable Software Performance Completions through Higher-Order Model Transformations

Chillies is a novel approach for variable model transformations closing the gap between abstract architecture models, used for performance prediction, and required low-level details. We enable variability of transformations using chain of generators based on the Higher-Order Transformation (HOT). HOTs target different goals, such as template instantiation or transformation composition. In addition, we discuss state-dependent behavior in prediction models and quality of model transformations

Neuromorphic deep convolutional neural network learning systems for FPGA in real time

Deep Learning algorithms have become one of the best approaches for pattern recognition in several fields, including computer vision, speech recognition, natural language processing, and audio recognition, among others. In image vision, convolutional neural networks stand out, due to their relatively simple supervised training and their efficiency extracting features from a scene. Nowadays, there exist several implementations of convolutional neural networks accelerators that manage to perform these networks in real time. However, the number of operations and power consumption of these implementations can be reduced using a different processing paradigm as neuromorphic engineering. Neuromorphic engineering field studies the behavior of biological and inner systems of the human neural processing with the purpose of design analog, digital or mixed-signal systems to solve problems inspired in how human brain performs complex tasks, replicating the behavior and properties of biological neurons. Neuromorphic engineering tries to give an answer to how our brain is capable to learn and perform complex tasks with high efficiency under the paradigm of spike-based computation. This thesis explores both frame-based and spike-based processing paradigms for the development of hardware architectures for visual pattern recognition based on convolutional neural networks. In this work, two FPGA implementations of convolutional neural networks accelerator architectures for frame-based using OpenCL and SoC technologies are presented. Followed by a novel neuromorphic convolution processor for spike-based processing paradigm, which implements the same behaviour of leaky integrate-and-fire neuron model. Furthermore, it reads the data in rows being able to perform multiple layers in the same chip. Finally, a novel FPGA implementation of Hierarchy of Time Surfaces algorithm and a new memory model for spike-based systems are proposed

Towards the systematic construction of domain-specific transformation languages

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-09195-2-13Proceedings of 10th European Conference, ECMFA 2014, Held as Part of STAF 2014, York, UK, July 21-25, 2014General-purpose transformation languages, like ATL or QVT, are the basis for model manipulation in Model-Driven Engineering (MDE). However, as MDE moves to more complex scenarios, there is the need for specialized transformation languages for activities like model merging, migration or aspect weaving, or for specific domains of wide use like UML. Such domain-specific transformation languages (DSTLs) encapsulate transformation knowledge within a language, enabling the reuse of recurrent solutions to transformation problems. Nowadays, many DSTLs are built in an ad-hoc manner, which requires a high development cost to achieve a full-featured implementation. Alternatively, they are realised by an embedding into general-purpose transformation or programming languages like ATL or Java. In this paper, we propose a framework for the systematic creation of DSTLs. First, we look into the characteristics of domain-specific transformation tools, deriving a categorization which is the basis of our framework. Then, we propose a domain-specific language to describe DSTLs, from which we derive a ready-to-run workbench which includes the abstract syntax, concrete syntax and translational semantics of the DSTL.This work has been funded by the Spanish Ministry of Economy and Competitivity with project “Go Lite” (TIN2011-24139

Toward the adaptation of component-based architectures by model transformation: behind smart user interfaces

Graphical user interfaces are not always developed for remaining static. There are GUIs with the need of implementing some variability mechanisms. Component-based GUIs are an ideal target for incorporating this kind of operations, because they can adapt their functionality at run-time when their structure is updated by adding or removing components or by modifying the relationships between them. Mashup user interfaces are a good example of this type of GUI, and they allow to combine services through the assembly of graphical components. We intend to adapt component based user interfaces for obtaining smart user interfaces. With this goal, our proposal attempts to adapt abstract component-based architectures by using model transformation. Our aim is to generate at run-time a dynamic model transformation, because the rules describing their behavior are not pre set but are selected from a repository depending on the context. The proposal describes an adaptation schema based on model transformation providing a solution to this dynamic transformation. Context information is processed to select at run-time a rule subset from a repository. Selected rules are used to generate, through a higher-order transformation, the dynamic model transformation. This approach has been tested through a case study which applies different repositories to the same architecture and context. Moreover, a web tool has been developed for validation and demonstration of its applicability. The novelty of our proposal arises from the adaptation schema that creates a non pre-set transformation, which enables the dynamic adaptation of component-based architectures

Toward the adaptation of component-based architectures by model transformation: behind smart user interfaces

Graphical user interfaces are not always developed for remaining static. There are GUIs with the need of implementing some variability mechanisms. Component-based GUIs are an ideal target for incorporating this kind of operations, because they can adapt their functionality at run-time when their structure is updated by adding or removing components or by modifying the relationships between them. Mashup user interfaces are a good example of this type of GUI, and they allow to combine services through the assembly of graphical components. We intend to adapt component based user interfaces for obtaining smart user interfaces. With this goal, our proposal attempts to adapt abstract component-based architectures by using model transformation. Our aim is to generate at run-time a dynamic model transformation, because the rules describing their behavior are not pre set but are selected from a repository depending on the context. The proposal describes an adaptation schema based on model transformation providing a solution to this dynamic transformation. Context information is processed to select at run-time a rule subset from a repository. Selected rules are used to generate, through a higher-order transformation, the dynamic model transformation. This approach has been tested through a case study which applies different repositories to the same architecture and context. Moreover, a web tool has been developed for validation and demonstration of its applicability. The novelty of our proposal arises from the adaptation schema that creates a non pre-set transformation, which enables the dynamic adaptation of component-based architectures

Event-based feature extraction using adaptive selection thresholds

Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems. These algorithms are often adapted to the event-based domain to perform online learning in neuromorphic hardware. However, not designed for the purpose, such algorithms typically require significant simplification during implementation to meet hardware constraints, creating trade offs with performance. Furthermore, conventional feature extraction algorithms are not designed to generate useful intermediary signals which are valuable only in the context of neuromorphic hardware limitations. In this work a novel event-based feature extraction method is proposed that focuses on these issues. The algorithm operates via simple adaptive selection thresholds which allow a simpler implementation of network homeostasis than previous works by trading off a small amount of information loss in the form of missed events that fall outside the selection thresholds. The behavior of the selection thresholds and the output of the network as a whole are shown to provide uniquely useful signals indicating network weight convergence without the need to access network weights. A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations. The use of selection thresholds is shown to produce network activation patterns that predict classification accuracy allowing rapid evaluation and optimization of system parameters without the need to run back-end classifiers. The feature extraction method is tested on both the N-MNIST (Neuromorphic-MNIST) benchmarking dataset and a dataset of airplanes passing through the field of view. Multiple configurations with different classifiers are tested with the results quantifying the resultant performance gains at each processing stage