DATE24 - Conference Proceedings Talk
Conference proceedings talk, Palacio De Congresos Valencia, Valencia, Spain
Presented a conference proceeding talk on optimizing object detection deep neural networks (DNNs) for edge devices, focusing on the role of context awareness in improving energy efficiency. The talk explored the inefficiencies of a one-size-fits-all approach in continuous mobile object detection (OD) tasks and introduced SHIFT, a framework that dynamically selects among multiple OD models based on contextual information and computational constraints. Additionally, the discussion highlighted how SHIFT leverages multi-accelerator execution to optimize energy efficiency while meeting latency requirements, achieving up to 7.5× energy savings and 2.8× latency reduction compared to state-of-the-art GPU-based single-model OD approaches.