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Found 3 papers, 0 papers with code
Enabling Weakly-Supervised Temporal Action Localization from On-Device Learning of the Video Stream
To enable W-TAL models to learn from a long, untrimmed streaming video, we propose an efficient video learning approach that can directly adapt to new environments.
Action Detection
Weakly-supervised Temporal Action Localization
+1
Sustainable AI Processing at the Edge
In particular, we explore the use of processing-in-memory (PIM) approaches, mobile GPU accelerators, and recently released FPGAs, and compare them with novel Racetrack memory PIM.
EF-Train: Enable Efficient On-device CNN Training on FPGA Through Data Reshaping for Online Adaptation or Personalization
In this work, we design EF-Train, an efficient DNN training accelerator with a unified channel-level parallelism-based convolution kernel that can achieve end-to-end training on resource-limited low-power edge-level FPGAs.
