Section: Scientific Foundations
Compilation and Synthesis for Reconfigurable Platforms
In spite of their advantages, reconfigurable architectures lack efficient and standardized compilation and design tools. As of today, this still makes the technology impractical for large scale industrial use. Generating and optimizing the mapping from high-level specifications to reconfigurable hardware platforms is therefore a key research issue, and the problem has received considerable interest over the last years  ,  ,  ,  . In the meantime, the complexity (and heterogeneity) of these platforms has also been increasing quite significantly, with complex heterogeneous multi-cores architectures becoming a de facto standard. As a consequence, the focus of designers is now geared toward optimizing overall system-level performance and efficiency  ,  ,  . Here again, existing tools are not well suited, as they fail at providing a unified programming view of the programmable and/or reconfigurable components implemented on the platform.
In this context we have been pursuing our efforts to propose tools whose design principles are based on a tight coupling between the compiler and the target hardware architectures. We build on the expertise of the team members in High Level Synthesis (HLS)  , ASIP optimizing compilers  and automatic parallelization for massively parallel specialized circuits  . We first study how to increase the efficiency of standard programmable processor by extending their instruction set to speed-up compute intensive kernels. Our focus is on efficient and exact algorithms for the identification, selection and scheduling of such instructions  . We also propose techniques to synthesize reconfigurable (or multi-mode) architectures. We address these challenges by borrowing techniques from high-level synthesis, optimizing compilers and automatic parallelization, especially when dealing with nested loop kernels. The goal is then either to derive a custom fine-grain parallel architecture and/or to derive the configuration of a Coarse Grain Reconfigurable Architecture (CGRA). In addition, and independently of the scientific challenges mentioned above, proposing such flows also poses significant software engineering issues. As a consequence, we also study how leading edge Object Oriented software engineering techniques (Model Driven Engineering) can help the Computer Aided Design (CAD) and optimizing compiler communities prototyping new research ideas.
Efficient implementation of multimedia and signal processing applications (in software for dsp cores or as special-purpose hardware) often requires, for reasons related to cost, power consumption or silicon area constraints, the use of fixed-point arithmetic, whereas the algorithms are usually specified in floating-point arithmetic. Unfortunately, fixed-point conversion is very challenging and time-consuming, typically demanding up to 50% of the total design or implementation time  . Thus, tools are required to automate this conversion. For hardware or software implementation, the aim is to optimize the fixed-point specification. The implementation cost is minimized under a numerical accuracy or an application performance constraint. For dsp -software implementation, methodologies have been proposed  ,  to achieve a conversion leading to an ANSI-C code with integer data types. For hardware implementation, the best results are obtained when the word-length optimization process is coupled with the high-level synthesis  ,  . Evaluating the effects of finite precision is one of the major and often the most time consuming step while performing fixed-point refinement. Indeed, in the word-length optimization process, the numerical accuracy is evaluated as soon as a new word-length is tested, thus, several times per iteration of the optimization process. Classical approaches are based on fixed-point simulations  ,  . They lead to long evaluation times and cannot be used to explore the entire design space. Therefore, our aim is to propose closed-form expressions of errors due to fixed-point approximations that are used by a fast analytical framework for accuracy evaluation.