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Dataplacement: Optimizing Data Residency

Updated 5 July 2026
  • Dataplacement is the strategic process of placing data, replicas, and tensor tiles across heterogeneous tiers to optimize cost, latency, and energy.
  • It employs complex optimization techniques such as ILPs, graph partitioning, and reinforcement learning to balance performance metrics with resource constraints.
  • Applications span geo-distributed clouds, multi-tenant systems, and on-chip accelerators, where effective placement decisions directly impact overall system efficiency.

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