Micro robots with artificial intelligence (AI) are being investigated for many applications, such as unmanned delivery services. The robots, shown in Fig. 14.3.1, have enhanced controllers that realize AI functions, such as perception (information extraction) and cognition (decision making). Historically, controllers have been based on general-purpose CPUs, and only recently, a few perception SoCs [1-3] have been reported. …

The robots with artificial intelligence (AI) are being investigated for many unmanned systems, such as unmanned delivery services. The robots are rapidly getting smaller and faster along with the advances in robotics technology, and they have to perform more complicated tasks in dynamically changing environments. Therefore, an ultra-low-power and high-performance artificial intelligence processor (AIP) is necessary for inte…

Advanced driver assistance system (ADAS) for adaptive cruise control and collision avoidance is strongly dependent upon the robust image recognition technology such as lane detection, vehicle/pedestrian detection and traffic sign recognition. However, the conventional ADAS cannot realize more advanced collision evasion in real environment due to the absence of intelligent vehicle/pedestrian behavior analysis. Moreover, accu…

Recently, intelligent robotics and automation have been widely used for various applications such as advanced driver assistance system (ADAS), autonomous driving technology, and robot control system. These require a real-time object detection as well as artificial intelligences (AI) such as path planning, cut in detection, behavior analysis, and localization. However, the heavy computation cost of those algorithms makes it …

Wearable head-mounted display (HMD) smart devices are emerging as a smartphone substitute due to their ease of use and suitability for advanced applications, such as gaming and augmented reality (AR). Most current HMD systems suffer from: 1) a lack of rich user interfaces, 2) short battery life, and 3) heavy weight. Although current HMDs (e.g. Google Glass) use a touch panel and voice commands as the interface, such interfa…

A low-power object recognition (OR) system with intuitive gaze user interface (UI) is proposed for battery-powered smart glasses. For low-power gaze UI, we propose a low-power single-chip gaze estimation sensor, called gaze image sensor (GIS). In GIS, a novel column-parallel pupil edge detection circuit (PEDC) with new pupil edge detection algorithm XY pupil detection (XY-PD) is proposed which results in 2.9x power reductio…

Real-time augmented reality (AR) is actively studied for the future user interface and experience in high-performance head-mounted display (HMD) systems. The small battery size and limited computing power of the current HMD, however, fail to implement the real-time markerless AR in the HMD. In this paper, we propose a real-time and low-power AR processor for advanced 3D-AR HMD applications. For the high throughput, the proc…

Object recognition processors have been reported for the applications of autonomic vehicle navigation, smart surveillance and unmanned air vehicles (UAVs). Most of the processors adopt a single classifier rather than multiple classifiers even though multi-classifier systems (MCSs) offer more accurate recognition with higher robustness. In addition, MCSs can incorporate the human vision system (HVS) recognition architecture …

A heterogeneous multi-core processor is proposed to achieve real-time dynamic object recognition on HD 720p video streams. The context-aware visual attention model is proposed to reduce the required computing power for HD object recognition based on enhanced attention accuracy. In order to realize real-time execution of the proposed algorithm, the processor adopts a 5-stage task-level pipeline that maximizes the utilization…

A heterogeneous many-core object recognition processor is proposed to realize robust and efficient object recognition on real-time video of cluttered scenes. Unlike previous approaches that simply aimed for high GOPS/W, we aim to achieve high Effective GOPS/W, or EGOPS/W, which only counts operations carried out on meaningful regions of an input image. This is achieved by the Unified Visual Attention Model (UVAM) which conf…

An attention controlled multi-core architecture is proposed for energy efficient object recognition. The proposed architecture employs two IP layers having different roles for energy efficient recognition processing: the attention/control IPs compute regions-of-interest (ROIs) of the entire image and control the multiple processing core stopper form local object recognition processing on selected area. To this end, a task m…

As object recognition requires huge computation power to deal with complex image processing tasks, it is very challenging to meet real-time processing demands under low-power constraints for embedded systems. In this paper, a configurable heterogeneous multi-core architecture with a dual-mode linear processor array and a cellular neural network on the network on chip platform is presented for real-time object recognition. T…

Real-time augmented reality (AR) is actively studied for the future user interface and experience in high-performance head-mounted display (HMD) systems. The small battery size and limited computing power of the current HMD, however, fail to implement the real-time markerless AR in the HMD. In this paper, we propose a real-time and low-power AR processor for advanced 3D-AR HMD applications. For the high throughput, the proc…