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Embedded machine learning aims to enable privacy-preserving, always-on intelligence at the edge. We focus on model compression and resource scheduling for efficient execution of deep neural networks under application-driven resource constraints.
The Experiment Orchestration Toolkit (ExOT) has been developed to evaluate covert channel or side channel attacks but can also be used to benchmark the performance of algorithms on a variety of platforms.
