Jean-Christophe Prévotet

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This paper describes the first implementation of a custom micro-kernel on a ARM-FPGA platform capable of managing reconfigurable hardware parts dynamically. After describing the structure of the proposed micro-kernel, we will focus on a custom specific system task dealing with the reconfiguration management, which is associated to a dedicated scheduling(More)
Reconfigurable resources are more and more envisaged inside System-on-Chip designs for facing with embedded computing power constraints. Moreover, a high level of flexibility of such platforms is required and can only be achieved with embedded software and real-time operating systems (RTOS) services. Unfortunately, this leads to very complex and(More)
Multiprocessor Systems-on-Chip (MPSoC) are becoming the standard high performance Digital Signal Processing (DSP) systems. Hardware complexity abstraction is needed to enable efficient MPSoC programming. A major challenge of MPSoC programming is efficiently handling the combination of new features necessary in a MPSoC operating system: load balancing and(More)
This paper presents the OveRSoC project. The objective is to develop an exploration and validation methodology of embedded real time operating systems for reconfigurable System-On-Chip based platforms. Here, we describe the overall methodology and the corresponding design environment. The method is based on abstract and modular SystemC models that allow to(More)
The virtualization technique has become a popular trend in the domain of real-time embedded systems. For example, in the automotive industry, practitioners are currently considering the idea of using such technique to run simultaneously the AUTOSAR real-time operating system (RTOS) for real-time programming, and the Linux-GENIVI operating system to support(More)
The data acquisition system for a new type of optical disdrometer is presented. As the device must measure sizes and velocities of raindrops as small as .1 mm diameter in real time in the presence of high noise and a variable baseline, algorithm design has been a challenge. The combining of standard signal processing techniques and machine learning methods(More)
High-energy physics experiments require high-speed triggering systems capable of performing complex pattern recognition at rates of Megahertz to Gigahertz. Neural networks implemented in hardware have been the solution of choice for certain experiments. The neural triggering problem is presented here via a detailed look at the H1 level 2 trigger at the HERA(More)