KAPLA: Pragmatic Representation and Fast Solving of Scalable NN Accelerator Dataflow

Dataflow scheduling decisions are of vital importance to neural network (NN) accelerators. Recent scalable NN accelerators support a rich set of advanced dataflow techniques. The problems of comprehensively representing and quickly finding optimized dataflow schemes thus become significantly more complicated and challenging. In this work, we first propose comprehensive and pragmatic dataflow representations for temporal and spatial scheduling on scalable multi-node NN architectures. An informal hierarchical taxonomy highlights the tight coupling across different levels of the dataflow space as the major difficulty for fast design exploration. A set of formal tensor-centric directives accurately express various inter-layer and intra-layer schemes, and allow for quickly determining their validity and efficiency. We then build a generic, optimized, and fast dataflow solver, KAPLA, which makes use of the pragmatic directives to explore the design space with effective validity check and efficiency estimation. KAPLA decouples the upper inter-layer level for fast pruning, and solves the lower intra-layer schemes with a novel bottom-up cost descending method. KAPLA achieves within only 2.2% and 7.7% energy overheads on the result dataflow for training and inference, respectively, compared to the exhaustively searched optimal schemes. It also outperforms random and machine-learning-based approaches, with more optimized results and orders of magnitude faster search speedup

Development of a hardware-In-the-Loop (HIL) testbed for cyber-physical security in smart buildings

As smart buildings move towards open communication technologies, providing access to the Building Automation System (BAS) through the intranet, or even remotely through the Internet, has become a common practice. However, BAS was historically developed as a closed environment and designed with limited cyber-security considerations. Thus, smart buildings are vulnerable to cyber-attacks with the increased accessibility. This study introduces the development and capability of a Hardware-in-the-Loop (HIL) testbed for testing and evaluating the cyber-physical security of typical BASs in smart buildings. The testbed consists of three subsystems: (1) a real-time HIL emulator simulating the behavior of a virtual building as well as the Heating, Ventilation, and Air Conditioning (HVAC) equipment via a dynamic simulation in Modelica; (2) a set of real HVAC controllers monitoring the virtual building operation and providing local control signals to control HVAC equipment in the HIL emulator; and (3) a BAS server along with a web-based service for users to fully access the schedule, setpoints, trends, alarms, and other control functions of the HVAC controllers remotely through the BACnet network. The server generates rule-based setpoints to local HVAC controllers. Based on these three subsystems, the HIL testbed supports attack/fault-free and attack/fault-injection experiments at various levels of the building system. The resulting test data can be used to inform the building community and support the cyber-physical security technology transfer to the building industry.Comment: Presented at the 2023 ASHRAE Winter Conferenc

Power-Line Extraction Method for UAV Point Cloud Based on Region Growing Algorithm

[Introduction] Since the power line has the characteristics of long transmission distance and a complex spatial environment, the UAV LiDAR point cloud technology can completely and efficiently obtain the geometric information of the power line and its surrounding spatial objects, and the existing supervised extraction and unsupervised extraction methods are deficient in point cloud data extraction in a large range of complex environments, according to the spatial environment characteristics of the main network and distribution network line point cloud data, a rapid extraction method of point cloud power line is proposed based on projection line characteristics and region growing algorithm. [Method] Firstly, in view of the characteristics that the overhead lines of the main network were usually higher than the surrounding spatial objects, the power lines were roughly extracted by the elevation histogram threshold method. Then, considering the characteristics that the vegetation canopy was higher than the distribution network line in the distribution network area, the KNN data points of the roughly extracted power line point cloud were obtained, and the point cloud was projected on the horizontal plane, and whether the point cloud was a power line point cloud was judged by the linear measurement of the point cloud. [Result] According to the existence of missing power line point clouds, all the power line point cloud clusters are obtained through a region growing mode, and on this basis, the catenary formula of each power line point cloud cluster is calculated through the catenary formula, and the point cloud with a fitting distance less than the threshold is merged as the same power line point cloud. [Conclusion] The proposed method aims at the problem of rapid power line extraction in inspection applications and overcomes the problem of power line point cloud missing and vegetation impact in the process of power line extraction, so this method can achieve power line point cloud extraction with high efficiency and accuracy

Genetic polymorphisms in plasminogen activator inhibitor-1 predict susceptibility to steroid-induced osteonecrosis of the femoral head in Chinese population

BACKGROUND: Steroid usage has been considered as a leading cause of non-traumatic osteonecrosis of the femoral head (ONFH), which is involved in hypo-fibrinolysis and blood supply interruption. Genetic polymorphisms in plasminogen activator inhibitor-1 (PAI-1) have been demonstrated to be associated with ONFH risk in several populations. However, this relationship has not been established in Chinese population. The aim of this study was to investigate the association of PAI-1 gene polymorphisms with steroid-induced ONFH in a large cohort of Chinese population. METHODS: A case–control study was conducted, which included 94 and 106 unrelated patients after steroid administration recruited from 14 provinces in China, respectively. Two SNPs (rs11178 and rs2227631) within PAI-1 were genotyped using Sequenom MassARRAY system. RESULTS: rs2227631 SNP was significantly associated with steroid-induced ONFH group in codominant (P = 0.04) and recessive (P = 0.02) models. However, there were no differences found in genotype frequencies of rs11178 SNP between controls and patients with steroid-induced ONFH (all P > 0.05). CONCLUSIONS: Our data offer the convincing evidence for the first time that rs2227631 SNP of PAI-1 may be associated with the risk of steroid-induced ONFH, suggesting that the genetic variations of this gene may play an important role in the disease development. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1569909986109783