A survey of on-device learning in TinyML organized by distribution change regimes, highlighting influences on applications, hardware, and solutions plus a gap between benchmarks and deployments.
In: 2025 IEEE International Parallel and Distributed Pro- cessing Symposium Workshops (IPDPSW)
5 Pith papers cite this work. Polarity classification is still indexing.
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GridPilot achieves 97.2 ms end-to-end grid response on a 3-GPU V100 testbed (6.9x faster than Nordic FFR requirement) and closes 2.5-5.8 pp cooling overhead via PUE-aware control in European grid replays.
MAMO uses multi-agent RL to automatically select reward weights for constrained optimization problems in non-stationary environments.
No single post-Moore technology replaces current HPC for plasma simulations, but FPGA-class accelerators offer near-term kernel offload, non-von Neumann architectures medium-term operator acceleration, and quantum computing long-term potential for warm dense matter microphysics.
Current SYCL implementations show inconsistencies in memory management (USM vs buffers) and kernel models (NDRange vs hierarchical) that reduce cross-platform reliability.
citing papers explorer
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What changes after deployment? A survey on On-device Learning in TinyML
A survey of on-device learning in TinyML organized by distribution change regimes, highlighting influences on applications, hardware, and solutions plus a gap between benchmarks and deployments.
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GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers
GridPilot achieves 97.2 ms end-to-end grid response on a 3-GPU V100 testbed (6.9x faster than Nordic FFR requirement) and closes 2.5-5.8 pp cooling overhead via PUE-aware control in European grid replays.
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A Multi-Agent system for Multi-Objective constrained optimization
MAMO uses multi-agent RL to automatically select reward weights for constrained optimization problems in non-stationary environments.
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Post-Moore Technologies for Plasma Simulation: A Community Roadmap
No single post-Moore technology replaces current HPC for plasma simulations, but FPGA-class accelerators offer near-term kernel offload, non-von Neumann architectures medium-term operator acceleration, and quantum computing long-term potential for warm dense matter microphysics.
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Evaluating SYCL as a Unified Programming Model for Heterogeneous Systems
Current SYCL implementations show inconsistencies in memory management (USM vs buffers) and kernel models (NDRange vs hierarchical) that reduce cross-platform reliability.