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through software programming to serve a wide range of AI-based applications. Because of the complexity of these designs, it is nowadays common to rely on end-to-end software compilation toolchains
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are long vector/matrix multicore architectures. The goal of our team is to develop effective runtime and compiler support to program these architectures using the SYCL, OpenMP and OneDNN programming models
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are long vector/matrix multicore architectures. The goal of our team is to develop effective runtime and compiler support to program these architectures using the SYCL, OpenMP and OneDNN programming models
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function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice of data structures, static analyses and compiler optimizations, parallelism and concurrency
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of data structures, static analyses and compiler optimizations, parallelism and concurrency) to turn these new theoretical developments into performant implementations; building state-of-the-art
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to improve the debugging capabilities of modern compiler toolchains. This role will contribute to the investigation and development of novel techniques for these, in the context of modern production