Publications associées à Tashkent+: Memory-Aware Load Balancing and Update Filtering in Replicated Databases

Long plasma duration operation analyses with an international multi-machine (tokamaks and stellarators) database

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When Your AI Becomes a Target: AI Security Incidents and Best Practices

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In contrast to vast academic efforts to study AI security, few real-world reports of AI security incidents exist. Released incidents prevent a thorough investigation of the attackers' motives, as crucial information about the company and AI application is ...

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EdgeAI-Aware Design of In-Memory Computing Architectures

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How to Achieve Large-Area Ultra-Fast Operation of MoS 2 Monolayer Flash Memories?

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Memory devices have returned to the spotlight due to increasing interest in using in-memory computing architectures to make data-driven algorithms more energy-efficient. One of the main advantages of this architecture is the efficient performance of vector ...

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Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...

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Accessing input data is a critical operation in data analytics: i) slow data access significantly degrades performance, and ii) storing everything in the fastest medium, i.e., memory, incurs high operational and hardware costs. Further, while GPUs offer in ...

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Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

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By supporting the access of multiple memory words at the same time, Bit-line Computing (BC) architectures allow the parallel execution of bit-wise operations in-memory. At the array periphery, arithmetic operations are then derived with little additional o ...

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