TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments

Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines: including image recognition, object detection, natural language processing, speech synthesis, and personalized recommendation pipelines. Cloud computing, as the de-facto backbone of modern computing infrastructure for both enterprise and consumer applications, has to be able to handle user-defined pipelines of diverse DNN inference workloads while maintaining isolation and latency guarantees, and minimizing resource waste. The current solution for guaranteeing isolation within FaaS is suboptimal -- suffering from "cold start" latency. A major cause of such inefficiency is the need to move large amount of model data within and across servers. We propose TrIMS as a novel solution to address these issues. Our proposed solution consists of a persistent model store across the GPU, CPU, local storage, and cloud storage hierarchy, an efficient resource management layer that provides isolation, and a succinct set of application APIs and container technologies for easy and transparent integration with FaaS, Deep Learning (DL) frameworks, and user code. We demonstrate our solution by interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x speedup in latency for image classification models and up to 210x speedup for large models. We achieve up to 8x system throughput improvement.Comment: In Proceedings CLOUD 201

Optically-Nonactive Assorted Helices Array with Interchangeable Magnetic/Electric Resonance

We report here the designing of optically-nonactive metamaterial by assembling metallic helices with different chirality. With linearly polarized incident light, pure electric or magnetic resonance can be selectively realized, which leads to negative permittivity or negative permeability accordingly. Further, we show that pure electric or magnetic resonance can be interchanged at the same frequency band by merely changing the polarization of incident light for 90 degrees. This design demonstrates a unique approach to construct metamaterial.Comment: 15 pages, 4 figure

Mitochondrial amyloid-beta peptide: Pathogenesis or late-phase development?

This is the publisher's version, also available electronically from http://iospress.metapress.com/content/8q4cf2u7gw6cllxt/?genre=article&issn=1387-2877&volume=9&issue=2&spage=127Mitochondrial and metabolic dysfunction have been linked to Alzheimer's disease for some time. Key questions regarding this association concern the nature and mechanisms of mitochondrial dysfunction, and whether such changes in metabolic properties are pathogenic or secondary, with respect to neuronal degeneration. In terms of mitochondria and Alzheimer's, altered function could reflect intrinsic properties of this organelle, potentially due to mutations in mitochondrial DNA, or extrinsic changes secondary to signal transduction mechanisms activated in the cytosol. This review presents data relevant to these questions, and considers the implication of recent findings demonstrating the presence of amyloid-β peptide in mitochondria, as well as intra-mitochondrial molecular targets with which it can interact. Regardless of the underlying mechanism(s), it is likely that mitochondrial dysfunction contributes to oxidant stress which is commonly observed in brains of patients with Alzheimer's and transgenic models of Alzheimer's-like pathology

Accelerating Reduction and Scan Using Tensor Core Units

Driven by deep learning, there has been a surge of specialized processors for matrix multiplication, referred to as TensorCore Units (TCUs). These TCUs are capable of performing matrix multiplications on small matrices (usually 4x4 or 16x16) to accelerate the convolutional and recurrent neural networks in deep learning workloads. In this paper we leverage NVIDIA's TCU to express both reduction and scan with matrix multiplication and show the benefits -- in terms of program simplicity, efficiency, and performance. Our algorithm exercises the NVIDIA TCUs which would otherwise be idle, achieves 89%-98% of peak memory copy bandwidth, and is orders of magnitude faster (up to 100x for reduction and 3x for scan) than state-of-the-art methods for small segment sizes -- common in machine learning and scientific applications. Our algorithm achieves this while decreasing the power consumption by up to 22% for reduction and16%for scan.Comment: In Proceedings of the ACM International Conference on Supercomputing (ICS '19

Chiral plasmonics and enhanced chiral light-matter interactions

International audienceChirality, which describes the broken mirror symmetry in geometric structures, exists macroscopically in our daily life as well as microscopically down to molecular levels. Correspondingly, chiral molecules interact differently with circularly polarized light exhibiting opposite handedness (left-handed and right-handed). However, the interaction between chiral molecules and chiral light is very weak. In contrast, artificial chiral plasmonic structures can generate “super-chiral” plasmonic near-field, leading to enhanced chiral light-matter (or chiroptical) interactions. The “super-chiral” near-field presents different amplitude and phase under opposite handedness incidence, which can be utilized to engineer linear and nonlinear chiroptical interactions. Specifically, in the interaction between quantum emitters and chiral plasmonic structures, the chiral hot spots can favour the emission with a specific handedness. This article reviews the state-of-the-art research on the design, fabrication and chiroptical response of different chiral plasmonic nanostructures or metasurfaces. This review also discusses enhanced chiral light-matter interactions that are essential for applications like chirality sensing, chiral selective light emitting and harvesting. In the final part, the review ends with a perspective on future directions of chiral plasmonics

SPEAL: Skeletal Prior Embedded Attention Learning for Cross-Source Point Cloud Registration

Point cloud registration, a fundamental task in 3D computer vision, has remained largely unexplored in cross-source point clouds and unstructured scenes. The primary challenges arise from noise, outliers, and variations in scale and density. However, neglected geometric natures of point clouds restricts the performance of current methods. In this paper, we propose a novel method termed SPEAL to leverage skeletal representations for effective learning of intrinsic topologies of point clouds, facilitating robust capture of geometric intricacy. Specifically, we design the Skeleton Extraction Module to extract skeleton points and skeletal features in an unsupervised manner, which is inherently robust to noise and density variances. Then, we propose the Skeleton-Aware GeoTransformer to encode high-level skeleton-aware features. It explicitly captures the topological natures and inter-point-cloud skeletal correlations with the noise-robust and density-invariant skeletal representations. Next, we introduce the Correspondence Dual-Sampler to facilitate correspondences by augmenting the correspondence set with skeletal correspondences. Furthermore, we construct a challenging novel large-scale cross-source point cloud dataset named KITTI CrossSource for benchmarking cross-source point cloud registration methods. Extensive quantitative and qualitative experiments are conducted to demonstrate our approach's superiority and robustness on both cross-source and same-source datasets. To the best of our knowledge, our approach is the first to facilitate point cloud registration with skeletal geometric priors.Comment: Accepted by AAAI202