Compilation and Optimizations for Efficient Machine Learning on Embedded Systems. (arXiv:2206.03326v2 [cs.LG] UPDATED)

Deep Neural Networks (DNNs) have achieved great success in a variety of
machine learning (ML) applications, delivering high-quality inferencing
solutions in computer vision, natural language processing, and virtual reality,
etc. However, DNN-based ML applications also bring much increased computational
and storage requirements, which are particularly challenging for embedded
systems with limited compute/storage resources, tight power budgets, and small
form factors. Challenges also come from the diverse application-specific
requirements, including real-time responses, high-throughput performance, and
reliable inference accuracy. To address these …

arxiv embedded learning lg machine machine learning systems