Tien-Ju Yang

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Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve(More)
Abstract A machine-learning based intelligent vision SoC implemented on a 9.3 mm die in a 40nm CMOS process is presented. The architecture realizes 140 meters active distance at 60fps and 60 meters at 300fps under Quad-VGA (1280×960) resolution while maintaining above 90% detection rate for versatile automotive applications. The system supports 64 object(More)
Owing to the diversity of projection surfaces, an effective mobile display system must be adaptive to the surface to avoid introducing a clipped scene. In this paper, we propose a smart mobile display system which automatically adapts to the location and motion of a surface. Firstly, an imperceptible structured light technique is adopted and continuous(More)
Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile devices, such as smartphones and wearable gadgets, where vision algorithms can enable many revolutionary real-world applications. The key limiting factor is the high energy consumption(More)
A sparse signal can be reconstructed from a small amount of random and linear measurements by solving a system of underdetermined equations. In this paper, we study the reconstruction problem while the system undergoes dynamic modifications. Resolving this problem from scratch requires high computational efforts. Therefore, we propose an efficient(More)
An efficient feature matching architecture targets at real-time video stabilization is revealed in this paper. For some applications, such as vehicular application, real-time video stabilization is needed to provide instant stable video input. However, feature matching is usually the bottleneck to achieve high performance. High speed feature matching(More)
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