SoC-Cluster as an Edge Server: an Application-driven Measurement Study

Huge electricity consumption is a severe issue for edge data centers. To this end, we propose a new form of edge server, namely SoC-Cluster, that orchestrates many low-power mobile system-on-chips (SoCs) through an on-chip network. For the first time, we have developed a concrete SoC-Cluster server that consists of 60 Qualcomm Snapdragon 865 SoCs in a 2U rack. Such a server has been commercialized successfully and deployed in large scale on edge clouds. The current dominant workload on those deployed SoC-Clusters is cloud gaming, as mobile SoCs can seamlessly run native mobile games. The primary goal of this work is to demystify whether SoC-Cluster can efficiently serve more general-purpose, edge-typical workloads. Therefore, we built a benchmark suite that leverages state-of-the-art libraries for two killer edge workloads, i.e., video transcoding and deep learning inference. The benchmark comprehensively reports the performance, power consumption, and other application-specific metrics. We then performed a thorough measurement study and directly compared SoC-Cluster with traditional edge servers (with Intel CPU and NVIDIA GPU) with respect to physical size, electricity, and billing. The results reveal the advantages of SoC-Cluster, especially its high energy efficiency and the ability to proportionally scale energy consumption with various incoming loads, as well as its limitations. The results also provide insightful implications and valuable guidance to further improve SoC-Cluster and land it in broader edge scenarios

Efficient and Layer-Dependent Exciton Pumping across Atomically Thin Organic–Inorganic Type-I Heterostructures

The fundamental light–matter interactions in monolayer transition metal dichalcogenides might be significantly engineered by hybridization with their organic counterparts, enabling intriguing optoelectronic applications. Here, atomically thin organic–inorganic (O–I) heterostructures, comprising monolayer MoSe2 and mono‐/few‐layer single‐crystal pentacene samples, are fabricated. These heterostructures show type‐I band alignments, allowing efficient and layer‐dependent exciton pumping across the O–I interfaces. The interfacial exciton pumping has much higher efficiency (>86 times) than the photoexcitation process in MoSe2, although the pentacene layer has much lower optical absorption than MoSe2. This highly enhanced pumping efficiency is attributed to the high quantum yield in pentacene and the ultrafast energy transfer between the O–I interface. Furthermore, those organic counterparts significantly modulate the bindings of charged excitons in monolayer MoSe2 via their precise dielectric environment engineering. The results open new avenues for exploring fundamental phenomena and novel optoelectronic applications using atomically thin O–I heterostructures.The authors also acknowledge financial support from ANU Ph.D. student scholarship, China Scholarship Council, ANU Major Equipment Committee fund (No. 14MEC34), and Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) (DE140100805). ARC Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET), ANU node; this work is also supported by NSFC 61734003, 61521001 and National Key Basic Research Program of China 2015CB921600

Geodesic flow kernel for unsupervised domain adaptation

In real-world applications of visual recognition, many factors—such as pose, illumination, or image quality—can cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. As such, the classifiers often perform poorly on the target domain. Domain adaptation techniques aim to correct the mismatch. Existing approaches have concentrated on learning feature representations that are invariant across domains, and they often do not directly exploit low-dimensional structures that are intrinsic to many vision datasets. In this paper, we propose a new kernel-based method that takes advantage of such structures. Our geodesic flow kernel models domain shift by integrating an infinite number of subspaces that characterize changes in geometric and statistical properties from the source to the target domain. Our approach is computationally advantageous, automatically inferring important algorithmic parameters without requiring extensive crossvalidation or labeled data from either domain. We also introduce a metric that reliably measures the adaptability between a pair of source and target domains. For a given target domain and several source domains, the metric can be used to automatically select the optimal source domain to adapt and avoid less desirable ones. Empirical studies on standard datasets demonstrate the advantages of our approach over competing methods. 1

?-phase modulated monolayer supercritical lens

10.1038/s41467-020-20278-xNature Communications1213