Vector Generation for Maximum Instantaneous Current Through Supply Lines for CMOS Circuits

Abstract We present two new algorithms for generating a small set of patterns for estimating the maximum instantaneous current through the power supply lines for CMOS circuits. The rst algorithm is based on timed ATPG, while the second is a probability-based approach. Both algorithms can handle circuits with arbitrary but known delays and they produce a set of 2-vector tests. Experimental results demonstrating that the outcome of applying our algorithms is a small set of patterns producing a current that is a tight l o w er bound on the maximum instantaneous current are included

Toward large-scale access-transistor-free memristive crossbars

Abstract — Memristive crossbars have been shown to be excel-lent candidates for building an ultra-dense memory system be-cause a per-cell access-transistor may no longer be necessary. However, the elimination of the access-transistor introduces sev-eral parasitic effects due to the existence of partially-selected de-vices during memory accesses, which could limit the scalability of access-transistor-free (ATF) memristive crossbars. In this paper we discuss these challenges in detail and describe some solutions addressing these challenges at multiple levels of design abstrac-tion. I

Towards a Smart Drone Cinematographer for Filming Human Motion

Affordable consumer drones have made capturing aerial footage more convenient and accessible. However, shooting cinematic motion videos using a drone is challenging because it requires users to analyze dynamic scenarios while operating the controller. In this thesis, our task is to develop an autonomous drone cinematography system to capture cinematic videos of human motion. We understand the system's filming performance to be influenced by three key components: 1) video quality metric, which measures the aesthetic quality -- the angle, the distance, the image composition -- of the captured video, 2) visual feature, which encapsulates the visual elements that influence the filming style, and 3) camera planning, which is a decision-making model that predicts the next best movement. By analyzing these three components, we designed two autonomous drone cinematography systems using both heuristic-based methods and learning-based methods.For the first system, we designed an Autonomous CinemaTography system -- "ACT" by proposing a viewpoint quality metric focusing on the visibility of the 3D human skeleton of the subject. We expanded the application of human motion analysis and simplified manual control by assisting viewpoint selection using a through-the-lens method. For the second system, we designed an imitation-based system that learns the artistic intention of the cameramen through watching professional aerial videos. We designed a camera planner that analyzes the video contents and previous camera motion to predict future camera motion. Furthermore, we propose a planning framework, which can imitate a filming style by ``seeing" only one single demonstration video of such style. We named it ``one-shot imitation filming." To the best of our knowledge, this is the first work that extends imitation learning to autonomous filming. Experimental results in both simulation and field test exhibit significant improvements over existing techniques and our approach managed to help inexperienced pilots capture cinematic videos

Graph Reasoning Transformer for Image Parsing

Capturing the long-range dependencies has empirically proven to be effective on a wide range of computer vision tasks. The progressive advances on this topic have been made through the employment of the transformer framework with the help of the multi-head attention mechanism. However, the attention-based image patch interaction potentially suffers from problems of redundant interactions of intra-class patches and unoriented interactions of inter-class patches. In this paper, we propose a novel Graph Reasoning Transformer (GReaT) for image parsing to enable image patches to interact following a relation reasoning pattern. Specifically, the linearly embedded image patches are first projected into the graph space, where each node represents the implicit visual center for a cluster of image patches and each edge reflects the relation weight between two adjacent nodes. After that, global relation reasoning is performed on this graph accordingly. Finally, all nodes including the relation information are mapped back into the original space for subsequent processes. Compared to the conventional transformer, GReaT has higher interaction efficiency and a more purposeful interaction pattern. Experiments are carried out on the challenging Cityscapes and ADE20K datasets. Results show that GReaT achieves consistent performance gains with slight computational overheads on the state-of-the-art transformer baselines.Comment: Accepted in ACM MM202