Evaluating Cache Coherent Shared Virtual Memory for Heterogeneous Multicore Chips

The trend in industry is towards heterogeneous multicore processors (HMCs), including chips with CPUs and massively-threaded throughput-oriented processors (MTTOPs) such as GPUs. Although current homogeneous chips tightly couple the cores with cache-coherent shared virtual memory (CCSVM), this is not the communication paradigm used by any current HMC. In this paper, we present a CCSVM design for a CPU/MTTOP chip, as well as an extension of the pthreads programming model, called xthreads, for programming this HMC. Our goal is to evaluate the potential performance benefits of tightly coupling heterogeneous cores with CCSVM

Why On-Chip Cache Coherence is Here to Stay

Today’s multicore chips commonly implement shared memory with cache coherence as low-level support for operating systems and application software. Technology trends continue to enable the scaling of the number of (processor) cores per chip. Because conventional wisdom says that the coherence does not scale well to many cores, some prognosticators predict the end of coherence. This paper refutes this conventional wisdom by showing one way to scale on-chip cache coherence with bounded costs by combining known techniques such as: shared caches augmented to track cached copies, explicit cache eviction notifications, and hierarchical design. Based upon our scalability analysis of this proof-of-concept design, we predict that on-chip coherence and the programming convenience and compatibility it provides are here to stay

Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery

The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. This pest has been nearly eradicated; however, southern part of Texas still faces this issue and is always prone to the pest reinfestation each year due to its sub-tropical climate where cotton plants can grow year-round. Volunteer cotton (VC) plants growing in the fields of inter-seasonal crops, like corn, can serve as hosts to these pests once they reach pin-head square stage (5-6 leaf stage) and therefore need to be detected, located, and destroyed or sprayed . In this paper, we present a study to detect VC plants in a corn field using YOLOv3 on three band aerial images collected by unmanned aircraft system (UAS). The two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be used for VC detection in a corn field using RGB (red, green, and blue) aerial images collected by UAS and (ii) to investigate the behavior of YOLOv3 on images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512, S3 pixels) based on average precision (AP), mean average precision (mAP) and F1-score at 95% confidence level. No significant differences existed for mAP among the three scales, while a significant difference was found for AP between S1 and S3 (p = 0.04) and S2 and S3 (p = 0.02). A significant difference was also found for F1-score between S2 and S3 (p = 0.02). The lack of significant differences of mAP at all the three scales indicated that the trained YOLOv3 model can be used on a computer vision-based remotely piloted aerial application system (RPAAS) for VC detection and spray application in near real-time.Comment: 38 Page

Computer Vision for Volunteer Cotton Detection in a Corn Field with UAS Remote Sensing Imagery and Spot Spray Applications

To control boll weevil (Anthonomus grandis L.) pest re-infestation in cotton fields, the current practices of volunteer cotton (VC) (Gossypium hirsutum L.) plant detection in fields of rotation crops like corn (Zea mays L.) and sorghum (Sorghum bicolor L.) involve manual field scouting at the edges of fields. This leads to many VC plants growing in the middle of fields remain undetected that continue to grow side by side along with corn and sorghum. When they reach pinhead squaring stage (5-6 leaves), they can serve as hosts for the boll weevil pests. Therefore, it is required to detect, locate and then precisely spot-spray them with chemicals. In this paper, we present the application of YOLOv5m on radiometrically and gamma-corrected low resolution (1.2 Megapixel) multispectral imagery for detecting and locating VC plants growing in the middle of tasseling (VT) growth stage of cornfield. Our results show that VC plants can be detected with a mean average precision (mAP) of 79% and classification accuracy of 78% on images of size 1207 x 923 pixels at an average inference speed of nearly 47 frames per second (FPS) on NVIDIA Tesla P100 GPU-16GB and 0.4 FPS on NVIDIA Jetson TX2 GPU. We also demonstrate the application of a customized unmanned aircraft systems (UAS) for spot-spray applications based on the developed computer vision (CV) algorithm and how it can be used for near real-time detection and mitigation of VC plants growing in corn fields for efficient management of the boll weevil pests.Comment: 39 page

Combined STN/SNr-DBS for the treatment of refractory gait disturbances in Parkinson's disease: study protocol for a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Severe gait disturbances in idiopathic Parkinson's disease (PD) are observed in up to 80% of all patients in advanced disease stages with important impact on quality of life. There is an unmet need for further symptomatic therapeutic strategies, particularly as gait disturbances generally respond unfavourably to dopaminergic medication and conventional deep brain stimulation of the subthalamic nucleus in advanced disease stages. Recent pathophysiological research pointed to nigro-pontine networks entrained to locomotor integration. Stimulation of the pedunculopontine nucleus is currently under investigation, however, hitherto remains controversial. The substantia nigra pars reticulata (SNr) - entrained into integrative locomotor networks - is pathologically overactive in PD. High-frequent stimulation of the substantia nigra pars reticulata preferentially modulated axial symptoms and therefore is suggested as a novel therapeutic candidate target for neuromodulation of refractory gait disturbances in PD.</p> <p>Methods</p> <p>12 patients with idiopathic Parkinson's disease and refractory gait disturbances under best individual subthalamic nucleus stimulation and dopaminergic medication will be enroled into this double-blind 2 × 2 cross-over clinical trial. The treatment consists of two different stimulation settings using <it>(i) </it>conventional stimulation of the subthalamic nucleus [STNmono] and <it>(ii) </it>combined stimulation of distant electrode contacts located in the subthalamic nucleus and caudal border zone of STN and substantia nigra pars reticulata [STN+SNr]. The primary outcome measure is the change of the cumulative 'axial score' (UPDRS II items '13-15' and UPRDS III items '27-31') at three weeks of constant stimulation in either condition. Secondary outcome measures include specific scores on freezing of gait, balance function, quality of life, non-motor symptoms, and neuropsychiatric symptoms. The aim of the present trial is to investigate the efficacy and safety of a three week constant combined stimulation on [STN+SNr] compared to [STNmono]. The results will clarify, whether stimulation on nigral contacts additional to subthalamic stimulation will improve therapeutic response of otherwise refractory gait disturbances in PD.</p> <p>Trial registration</p> <p>The trial was registered with the clinical trials register of <url>http://www.clinicaltrials.gov</url> (<a href="http://www.clinicaltrials.gov/ct2/show/NCT01355835">NCT01355835</a>)</p