Aurelien Bloch
École Polytechnique Fédérale de Lausanne
5 Papers
1 Citations
Aurelien Bloch is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Dataflow & Design space exploration. The author has an hindex of 1, co-authored 3 publications.
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Papers
Programming Heterogeneous CPU-GPU Systems by High-Level Dataflow Synthesis
Aurelien Bloch,Endri Bezati,Marco Mattavelli +2 more
- 01 Oct 2020
TL;DR: A dataflow based approach for the synthesis of applications to be executed on mixed CPU and GPU architectures that provides portability of applications on CPUs, GPUs, and mixed CPU/GPU architectures as well as the possibility of exploring the design space of all partitioning options without the need of rewriting the application code.
4
Profiling of dynamic dataflow programs on MPSoC multi-core architectures
Simone Casale Brunet,Endri Bezati,Aurelien Bloch,Marco Mattavelli +3 more
- 01 Oct 2017
TL;DR: The methodology has been developed and validated using different application design based on dynamic dataflow program implementations running on a multi-core ARM MPSoC architecture and its implementation applied to dynamic data flow programs running on embedded heterogeneous platforms.
2
Performance Estimation of High-Level Dataflow Program on Heterogeneous Platforms by Dynamic Network Execution
TL;DR: In this paper , the authors describe a method for the clock-accurate profiling of software applications developed using the dataflow programming paradigm such as the formal RVL-CAL language.
Dynamic SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis
TL;DR: In this article , a methodology composed of several stages for enhancing the performance of dataflow software developed in RVC-CAL and generating low-level implementations to be executed on GPU/CPU heterogeneous hardware platforms is presented.
Composite Data Types in Dynamic Dataflow Languages as Copyless Memory Sharing Mechanism
Aurelien Bloch,Endri Bezati,Marco Mattavelli +2 more
- 12 Jun 2019
TL;DR: New optimization approaches aiming at reducing the impact of memory accesses on the performance of dataflow programs are presented and the context, the definition of the optimization problem and how it can be applied to dataflow designs are described.