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Scaling analog in-memory computing towards large generative AI models H/F Subject Your work will span device-to-algorithm co-design, focusing on state-of-the-art small language model engineering and
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algorithm-hardware co-design of adaptive Transformer architectures using ReRAM analog in-memory computing. Using PyTorch and in-house ReRAM characterization data, you will develop reference models and
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will initially focus on algorithm development and simulation. Starting from reference methods, you will evaluate different strategies and progressively incorporate realistic ReRAM device characteristics
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will involve: - Understanding the various sensors. - Implementing a calibration algorithm using AI-based methods. - Adopting a rigorous scientific approach to validate the relevance of the proposed
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[4] M. Josien and R. Prat, Parallel and bias-free RSA algorithm for maximal Poisson-sphere sampling, Computer Physics Communications Moyens / Méthodes / Logiciels RSA, Mérope, ExaNBody Profil du