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As deep neural networks (DNNs) prove their importance and feasibility, moreand more DNN-based apps, such as detection and classification of objects, havebeen developed and deployed on autonomous vehicles (AVs). To meet their growingexpectations and requirements, AVs should "optimize" use of their limitedonboard computing resources for multiple concurrent in-vehicle apps whilesatisfying their timing requirements (especially for safety). That is,real-time AV apps should share the limited on-board resources with otherconcurrent apps without missing their deadlines dictated by the frame rate of acamera that generates and provides input images to the apps. However, most, ifnot all, of existing DNN solutions focus on enhancing the concurrency of theirspecific hardware without dynamically optimizing/modifying the DNN apps'resource requirements, subject to the number of running apps, owing to theirhigh computational cost. To mitigate this limitation, we propose DynaMIX(Dynamic MIXed-precision model construction), which optimizes the resourcerequirement of concurrent apps and aims to maximize execution accuracy. Torealize a real-time resource optimization, we formulate an optimization problemusing app performance profiles to consider both the accuracy and worst-caselatency of each app. We also propose dynamic model reconfiguration by lazyloading only the selected layers at runtime to reduce the overhead of loadingthe entire model. DynaMIX is evaluated in terms of constraint satisfaction andinference accuracy for a multi-tasking system and compared againststate-of-the-art solutions, demonstrating its effectiveness and feasibilityunder various environmental/operating conditions.

DynaMIX: Resource Optimization for DNN-Based Real-Time Applications on a Multi-Tasking System