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Imec

Develop an end-to-end simulation and modeling framework that helps evaluate power and performance of various in-memory & near-memory compute architectures for training supervised/unsupervised and reinforcement learning algorithms.
Explore novel compute architectures, dataflow, and memory organization that helps reduce power consumption during training leveraging in-memory and near-memory compute.
Evaluate the impact of various novel technologies and memory devices on compute for AI workloads.

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