Online training is essential to maintain a high object detection (OD) in various environments. However, additional computation workload, EMA, and high bit precision is the problem of conventional online learning scheme on mobile devices. Therefore, a low power real-time online learning OD processor is proposed with three key features. In this paper, we present low power online learning processor for mobile devices with 3 key features: 1) Multiscale linear quantization and architecture to support it for low-bit fxp-based arithmetic at all stages of online learning. 2) Low-gradient channel skipping for computation reduction and EMA reduction. 3) Gradient Norm Estimation to support gradient norm clipping with less than 0.1% additional computations for fast adaptation. As a result, the proposed processor achieves 34 frame-per-second real-time OD with accurate online learning while only consuming 49.5mW.
Related papers:
Song, Seokchan, et al. “A 49.5 mW Multi-scale Linear Quantized Online Learning Processor for Real-Time Adaptive Object Detection.” IEEE Transactions on Circuits and Systems II: Express Briefs (2022).
Song, Seokchan, et al. “A 49.5 mW Multi-Scale Linear Quantized Online Learning Processor for Real-Time Adaptive Object Detection”, IEEE International Symposium on Circuits and Systems (ISCAS), May. 2022
