Abstract
This paper describes MATISSE, a MATLAB to C compiler targeting embedded systems that is based on Strategic and Aspect-Oriented Programming concepts. MATISSE takes as input: (1) MATLAB code and (2) LARA aspects related to types and shapes, code insertion/ removal, and specialization based directives defining default variable values. In this paper we also illustrate the use of MATISSE in leveraging data types and shapes to generate customized C code suitable for high-level hardware synthesis tools. The preliminary experimental results presented here reveal the described approach to yield performance results for the resulting hardware and software references implementations that are comparable in terms of performance with hand-crafted solutions but derived automatically at a fraction of the cost.

Abstract
In this paper we present a clustering-based selection approach for reducing the number of compilation passes used in search space during the exploration of optimizations aiming at increasing the performance of a given function and/or code fragment. The basic idea is to identify similarities among functions and to use the passes previously explored each time a new function is being compiled. This subset of compiler optimizations is then used by a Design Space Exploration (DSE) process. The identification of similarities is obtained by a data mining method which is applied to a symbolic code representation that translates the main structures of the source code to a sequence of symbols based on transformation rules. Experiments were performed for evaluating the effectiveness of the proposed approach. The selection of compiler optimization sequences considering a set. of 49 compilation passes and targeting a Xilinx Nlicrofilaze processor was performed aiming at latency improvements for 41 functions from Texas Instruments benchmarks. The results reveal that the passes selection based on our clustering method achieves a significant gain on execution time over the full search space still achieving important performance speedups.

Abstract
Matrix and data manipulation programming languages are an essential tool for data analysts. However, these languages are often unstructured and lack modularity mechanisms. This paper presents a business intelligence approach for studying the manifestations of lack of modularity support in that kind of languages. The study is focused on MATLAB as a well established representative of those languages. We present a technique for the automatic detection and quantification of concerns in MATLAB, as well as their exploration in a code base. Ubiquitous Self Organizing Map (UbiSOM) is used based on direct usage of indicators representing different sets of tokens in the code. UbiSOM is quite effective to detect patterns of co-occurrence between multiple concerns. To illustrate, a repository comprising over 35, 000 MATLAB files is analyzed using the technique and relevant conclusions are drawn.

Abstract
In many engineering and science areas, models are developed and validated using high-level programing languages and environments as is the case with MATLAB. In order to target the multicore heterogeneous architectures being used on embedded systems to provide high performance computing with acceptable energy/power envelops, developers manually migrate critical code sections to lower-level languages such as C and OpenCL, a time consuming and error prone process. Thus, automatic source-to-source approaches are highly desirable. We present an approach to compile MATLAB and output equivalent C/OpenCL code to target architectures, such as GPU based hardware accelerators. We evaluate our approach on an existing MATLAB compiler framework named MATISSE. The OpenCL generation relies on the manual insertion of directives to guide the compilation and is also capable of generating C wrapper code to interface and synchronize with the OpenCL code. We evaluated the compiler with a number of benchmarks from different domains and the results are very encouraging.

Abstract
MATLAB is a high-level language used in various scientific and engineering fields. Deployment of well-Tested MATLAB code to production would be highly desirable, but in practice a number of obstacles prevent this, notably performance and portability. Although MATLAB-To-C compilers exist, the performance of the generated C code may not be sufficient and thus it is important to research alternatives, such as CPU parallelism, GPGPU computing and FPGAS. OpenCL is an API and programming language that allows targeting these devices, hence the motivation for MATLAB-To-OpenCL compilation. In this paper, we describe our recent efforts on offloading code to OpenCL devices in the context of our MATLAB to C/OpenCL compiler. © 2017 ACM.

Abstract
The synthesis and mapping of applications to configurable embedded systems is a notoriously complex process. Design-flows typically include tools that have a wide range of parameters which interact in very unpredictable ways, thus creating a large and complex design space. When exploring this space, designers must manage the interfaces between different tools and apply, often manually, a sequence of tool-specific transformations making design exploration extremely cumbersome and error-prone. This paper describes the use of techniques inspired by aspect-oriented technology and scripting languages for defining and exploring hardware compilation strategies. In particular, our approach allows developers to control all stages of a hardware/software compilation and synthesis toolchain: from code transformations and compiler optimizations to placement and routing for tuning the performance of application kernels. Our approach takes advantage of an integrated framework which provides a transparent and unified view over toolchains, their data output and the control of their execution. We illustrate the use of our approach when designing application-specific hardware architectures generated by a toolchain composed of high-level source-code transformation and synthesis tools. The results show the impact of various strategies when targeting custom hardware and expose the complexities in devising these strategies, hence highlighting the productivity benefits of this approach.