Abstract-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 of its 31 heterogeneous cores, comprising four simultaneous multithreading feature extraction clusters, a cache-based feature matching processor and a machine learning engine. Dynamic resource management is applied to adaptively tune thread allocation and power management during execution based on the detected amount of tasks and hardware utilization to increase energy efficiency. As a result, the 32 mm chip, fabricated in 0.13 m CMOS technology, achieves 30 frame/sec with 342 8-bit GOPS peak performance and 320 mW average power dissipation, which are a 2.72 times performance improvement and 2.54 times per-pixel energy reduction compared to the previous state-of-the-art.Index Terms-Multi-core processor, object recognition, scale invariant feature transform, heterogeneous, low power processor, dynamic resource management, dynamic voltage and frequency scaling.
GLOSSARY OF ABBREVIATIONS
GOPS
Augmented reality (AR) is being investigated in advanced displays for the augmentation of images in a real-world environment. Wearable systems, such as head-mounted display (HMD) systems, have attempted to support real-time AR as a next generation UI/UX [1-2], but have failed, due to their limited computing power. In a prior work, a chip with limited AR functionality was reported that could perform AR with the help of markers placed in the environment (usually 1D or 2D bar codes) [3]. However, for a seamless visual experience, 3D objects should be rendered directly on the natural video image without any markers. Unlike marker-based AR, markerless AR requires natural feature extraction, general object recognition, 3D reconstruction, and camera-pose estimation to be performed in parallel. For instance, markerless AR for a VGA input-test video consumes ~1.3W power at 0.2fps throughput, with TI's OMAP4430, which exceeds power limits for wearable devices. Consequently, there is a need for a high-performance energy-efficient markerless AR processor to realize a real-time AR system, especially for HMD applications.
Approximate nearest neighbor searching has been studied as the keypoint matching algorithm for object recognition systems, and its hardware realization has reduced the external memory access which is the main bottleneck in object recognition process. However, external memory access reduction alone cannot satisfy the ever-increasing memory bandwidth requirement due to the rapid increase of the image resolution and frame rate of many recent applications such as advanced driver assistance system. In this paper, vocabulary forest (VF) processor is proposed that achieves both high accuracy and high speed by integrating on-chip database (DB) to remove external memory access. The area-efficient reusable-vocabulary tree architecture is proposed to reduce area, and the propagate-and-compute-array architecture is proposed to enhance the processing speed of the VF. The proposed VF processor can speed up the object matching stage by 16.4x compared with the state-of-the-art matching processor [Hong et al., Symp. VLSIC, 2013] for high resolution (Full-HD) and real-time (60 fps) video object recognition. It is fabricated using 65 nm CMOS technology and integrated into an object recognition SoC. The proposed VF chip achieves 2.07 M-vector/s throughput and 13.3 nJ/vector per-vector energy with 95.7% matching accuracy for 100 objects.
Background and Aims
Genome-wide association studies (GWAS) of inflammatory bowel disease (IBD) in multiple populations have identified over 240 susceptibility loci. We previously performed a largest-to-date Asian-specific IBD GWAS to identify 2 new IBD risk loci and confirm associations with 28 established loci. To identify additional susceptibility loci in Asians, we expanded our previous study design by doubling the case size with an additional data set of 1,726 cases and 378 controls.
Methods
An inverse-variance fixed-effects meta-analysis was performed between the previous and the new GWAS dataset, comprising a total of 3,195 cases and 4,419 controls, followed by replication in an additional 1,088 cases and 845 controls.
Results
The meta-analysis of Korean GWAS identified 1 novel locus for ulcerative colitis at rs76227733 on 10q24 (pcombined = 6.56 × 10 -9) and 2 novel loci for Crohn’s disease (CD) at rs2240751 on 19p13 (pcombined = 3.03 × 10 -8) and rs6936629 in on 6q22 (pcombined = 3.63 × 10 -8). Pathway-based analysis of GWAS data using MAGMA showed that MHC and antigenic stimulus-related pathways were more significant in Korean CD, whereas cytokine and transcription factor-related pathways were more significant in European CD. Phenotype variance explained by the polygenic risk scores derived from Korean data explained up to 14 % of variance of CD whereas those derived from European data explained 10%, emphasizing the need for large-scale genetic studies in this population.
Conclusions
The identification of novel loci not previously associated with IBD suggest the importance of studying the inflammatory bowel disease genetics in diverse populations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.