A low power real-time visual object tracking (VOT) processor using the siamese network (SiamNet) is proposed for mobile devices. Two key features enable a real-time VOT with low power consumption on mobile devices. First, correlation-based spatial early stopping (CSES) is proposed to reduce the computational workload. CSES reduces ~56.8% of the overall computation of the SiamNet by gradually eliminating the background. Second, the dual mode reuse core (DMRC) is proposed for supporting both the convolution layer and the cross-correlation layer with high core utilization. Finally, the proposed VOT processor is implemented in 28 nm CMOS technology and occupies 0.42 mm 2 . The proposed processor achieves 0.587 for the success rate and 0.778 for the precision in the OTB-100 dataset with SiamRPN++-AlexNet. Compared to previous VOT processors, the proposed processor shows state-of-the-art performance while showing lower power consumption. The proposed processor achieves 64.1 mW peak power and 58.2 mW tracking power consumption at 32.1 frame-per-second (fps) real-time VOT on mobile devices.

中文翻译：

---

暹罗网络上具有空间早停功能的 64.1mW 精确实时视觉目标跟踪处理器


为移动设备提出了一种使用暹罗网络（SiamNet）的低功耗实时视觉对象跟踪（VOT）处理器。两个关键功能可在移动设备上实现低功耗的实时 VOT。首先，提出了基于相关性的空间早停（CSES）来减少计算工作量。 CSES 通过逐渐消除背景，减少了 SiamNet 整体计算量的约 56.8%。其次，提出了双模式重用核心（DMRC），以支持高核心利用率的卷积层和互相关层。最后，所提出的VOT处理器采用28 nm CMOS技术实现，占据0.42 mm 2 。所提出的处理器在 SiamRPN++-AlexNet 的 OTB-100 数据集中实现了 0.587 的成功率和 0.778 的精度。与之前的 VOT 处理器相比，该处理器展现了最先进的性能，同时功耗更低。该处理器在移动设备上以每秒 32.1 帧 (fps) 的实时 VOT 速度实现 64.1 mW 峰值功率和 58.2 mW 跟踪功耗。