Abstract
Edge computing is an attractive avenue to support low-latency applications including those that leverage deep learning (DL)-based model inferencing. Due to constraints on compute, storage and power at the edge, however, these DL models must be quantized to reduce their footprint while minimizing loss of accuracy. However, DL models and their quantized equivalents are often prone to adversarial attacks requiring them to be made robust against such attacks. The resource constraints at the edge, however, preclude any quantization and robustness design operations directly at the edge. Moreover, the changing dynamics of edge-based computations and resulting concept drifts in the models require an iterative approach to meet the needs of robust DL models at the edge. To address these challenges, this paper presents initial results on an iterative procedure involving a DDDAS feedback loop. DDDAS is used to dynamically instrument the edge-deployed, quantized DL models for data on the effectiveness of their quantization and robustness abilities, which in turn is used to drive an automated, cloud-based process that uses tools, such as Apache TVM, to generate quantized, optimized and robust DL models suitable for the edge. These models subsequently are automatically deployed at the edge using orchestration tools. Preliminary studies using this approach have shown its effectiveness in image classification and object detection applications.
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Canady, R., Zhou, X., Barve, Y., Balasubramanian, D., Gokhale, A. (2024). Applying DDDAS Principles for Realizing Optimized and Robust Deep Learning Models at the Edge. In: Blasch, E., Darema, F., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2022. Lecture Notes in Computer Science, vol 13984. Springer, Cham. https://doi.org/10.1007/978-3-031-52670-1_32
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