Abstract
The development of applications for high-performance embedded systems is a long and error-prone process because in addition to the required functionality, developers must consider various and often conflicting nonfunctional requirements such as performance and/or energy efficiency. The complexity of this process is further exacerbated by the multitude of target architectures and mapping tools. This article describes LARA, an aspect-oriented programming language that allows programmers to convey domain-specific knowledge and nonfunctional requirements to a toolchain composed of source-to-source transformers, compiler optimizers, and mapping/synthesis tools. LARA is sufficiently flexible to target different tools and host languages while also allowing the specification of compilation strategies to enable efficient generation of software code and hardware cores (using hardware description languages) for hybrid target architectures - a unique feature to the best of our knowledge not found in any other aspect-oriented programming language. A key feature of LARA is its ability to deal with different models of join points, actions, and attributes. In this article, we describe the LARA approach and evaluate its impact on code instrumentation and analysis and on selecting critical code sections to be migrated to hardware accelerators for two embedded applications from industry. Copyright (c) 2014 John Wiley & Sons, Ltd.

Abstract
Runtime adaptability is expected to adjust the application and the mapping of computations according to usage contexts, operating environments, resources availability, etc. However, extending applications with adaptive features can be a complex task, especially due to the current lack of programming models and compiler support. One of the runtime adaptability possibilities is the use of specialized code according to data workloads and environments. Traditional approaches use multiple code versions generated offline and, during runtime, a strategy is responsible to select a code version. Moving code generation to runtime can achieve important improvements but may impose unacceptable overhead. This paper presents an aspect-oriented programming approach for runtime adaptability. We focus on a separation of concerns (strategies vs. application) promoted by a domain-specific language for programming runtime strategies. Our strategies allow runtime specialization based on contextual information. We use a template-based runtime code generation approach to achieve program specialization. We demonstrate our approach with examples from image processing, which depict the benefits of runtime specialization and illustrate how several factors need to be considered to eficiently adapt the application. © 2015 ACM.

Abstract
In the context of the REFLECT project[1] we have developed an aspect-oriented compilation and synthesis toolchain that aims at facilitating the mapping of applications described in high-level imperative programming languages, such as C, to heterogeneous and configurable computing systems. More specifically, we have designed an aspect-oriented domain-specific language, called LARA[2], that allows programmers to convey application-specific and domain-specific knowledge as a way to capture non-functional concerns. The LARA specifications and the subsequent control of the tools via a code weaver allows a seamless exploration of alternative designs and run-time adaptive strategies, in effect enabling designspace exploration (DSE). © 2013 Springer-Verlag.

Abstract
This article presents an approach to enrich the MATLAB(1) language with aspect-oriented modularity features, enabling developers to experiment different implementation characteristics and to acquire runtime data and traces without polluting their base MATLAB code. We propose a language through which programmers configure the low-level data representation of variables and expressions. Examples include specifically-tailored fixed-point data representations leading to more efficient support for the underlying hardware, e.g., digital signal processors and application-specific architectures, without built-in floating point units. This approach assists developers in adding handlers and monitoring features in a non-invasive way as well as configuring MATLAB functions with optimized implementations. Different aspect modules can be used to retarget common MATLAB code bases for different purposes and implementations. We validate the proposed approach with a set of representative examples where we attain a simple way to explore a number of properties. Experiment results and collected aspect-oriented software metrics lend support to the claims on its usefulness.

Abstract
This chapter describes a series of experiments aimed at evaluating the effectiveness of the REFLECT design-flow in terms of ease of use and quality of the generated designs. In these experiments, we exercised the use of LARA to control and guide the REFLECT design-flow components, such as the Harmonic weaver, the CoSy-based compilers, and the back-end Molen/ML510 toolchain. Various research results have been presented in previous publications focusing on specific aspects of the REFLECT design-flow [1], including strategies for optimizing hardware/software systems [2], strategies for optimizing hardware synthesis [3], strategies for hardware/software specialization [4], strategies for resource efficiency [5], and strategies addressing safety requirements [6, 7]. © Springer Science+Business Media New York 2013. All rights are reserved.

Abstract
A common migration path for applications to high-performance multicore architectures relies on code annotations with concurrent semantics. Some annotations, however, are very target architecture specific and thus highly non-portable. In this paper we describe a source-to-source code transformation system that allows programmers to specify transformations using an aspect-oriented domain specific language - LARA. LARA allows programmers to specify strategies to search large code transformation design spaces while preserving the original source code. As the experimental results reveal, this approach leads to a substantial reduction in code maintenance costs, and promotes the portability of both programmers and performance. Copyright © 2014 ACM.