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"abstract": "Spiking neural networks (SNNs) are promising alternatives to artificial neural networks (ANNs) since they are more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatiotemporal sparsity; thus, they are helpful in enabling energy-efficient hardware inference. However, exploiting the spatiotemporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading the energy efficiency. Compared to SNNs with simple fully connected structures, those extensive structures (e.g., standard convolutions, depthwise convolutions, and pointwise convolutions) can deal with more complicated tasks but lead to difficulties in hardware mapping. In this work, we propose a novel reconfigurable architecture, Cerebron, which can fully exploit the spatiotemporal sparsity in SNNs with maximized data reuse and propose optimization techniques to improve the efficiency and flexibility of the hardware. To achieve flexibility, the reconfigurable compute engine is compatible with a variety of spiking layers and supports inter-computing-unit (CU) and intra-CU reconfiguration. The compute engine can exploit data reuse and guarantee parallel data access when processing different convolutions to achieve memory efficiency. A two-step data sparsity exploitation method is introduced to leverage the sparsity of discrete spikes and reduce the computation time. Besides, an online channelwise workload scheduling strategy is designed to reduce the latency further. Cerebron is verified on image segmentation and classification tasks using a variety of state-of-the-art spiking network structures. Experimental results show that Cerebron has achieved at least <inline-formula> <tex-math notation=\"LaTeX\">Z_$17.5\\times $_Z</tex-math></inline-formula> prediction energy reduction and <inline-formula> <tex-math notation=\"LaTeX\">Z_$20\\times $_Z</tex-math></inline-formula> speedup compared with state-of-the-art field-programmable gate array (FPGA)-based accelerators.",
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"content": "Spiking neural networks (SNNs) are promising alternatives to artificial neural networks (ANNs) since they are more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatiotemporal sparsity; thus, they are helpful in enabling energy-efficient hardware inference. However, exploiting the spatiotemporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading the energy efficiency. Compared to SNNs with simple fully connected structures, those extensive structures (e.g., standard convolutions, depthwise convolutions, and pointwise convolutions) can deal with more complicated tasks but lead to difficulties in hardware mapping. In this work, we propose a novel reconfigurable architecture, Cerebron, which can fully exploit the spatiotemporal sparsity in SNNs with maximized data reuse and propose optimization techniques to improve the efficiency and flexibility of the hardware. To achieve flexibility, the reconfigurable compute engine is compatible with a variety of spiking layers and supports inter-computing-unit (CU) and intra-CU reconfiguration. The compute engine can exploit data reuse and guarantee parallel data access when processing different convolutions to achieve memory efficiency. A two-step data sparsity exploitation method is introduced to leverage the sparsity of discrete spikes and reduce the computation time. Besides, an online channelwise workload scheduling strategy is designed to reduce the latency further. Cerebron is verified on image segmentation and classification tasks using a variety of state-of-the-art spiking network structures. Experimental results show that Cerebron has achieved at least <inline-formula> <tex-math notation=\"LaTeX\">$17.5\\times $ </tex-math></inline-formula> prediction energy reduction and <inline-formula> <tex-math notation=\"LaTeX\">$20\\times $ </tex-math></inline-formula> speedup compared with state-of-the-art field-programmable gate array (FPGA)-based accelerators.",
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"normalizedAbstract": "Spiking neural networks (SNNs) are promising alternatives to artificial neural networks (ANNs) since they are more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatiotemporal sparsity; thus, they are helpful in enabling energy-efficient hardware inference. However, exploiting the spatiotemporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading the energy efficiency. Compared to SNNs with simple fully connected structures, those extensive structures (e.g., standard convolutions, depthwise convolutions, and pointwise convolutions) can deal with more complicated tasks but lead to difficulties in hardware mapping. In this work, we propose a novel reconfigurable architecture, Cerebron, which can fully exploit the spatiotemporal sparsity in SNNs with maximized data reuse and propose optimization techniques to improve the efficiency and flexibility of the hardware. To achieve flexibility, the reconfigurable compute engine is compatible with a variety of spiking layers and supports inter-computing-unit (CU) and intra-CU reconfiguration. The compute engine can exploit data reuse and guarantee parallel data access when processing different convolutions to achieve memory efficiency. A two-step data sparsity exploitation method is introduced to leverage the sparsity of discrete spikes and reduce the computation time. Besides, an online channelwise workload scheduling strategy is designed to reduce the latency further. Cerebron is verified on image segmentation and classification tasks using a variety of state-of-the-art spiking network structures. Experimental results show that Cerebron has achieved at least - prediction energy reduction and - speedup compared with state-of-the-art field-programmable gate array (FPGA)-based accelerators.",
"title": "Cerebron: A Reconfigurable Architecture for Spatiotemporal Sparse Spiking Neural Networks",
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"Energy Efficiency",
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"Neurons",
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"givenName": "Qinyu",
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"surname": "Gao",
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"affiliation": "Department of Microelectronics, Delft University of Technology, Delft, The Netherlands",
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"surname": "Fu",
"fullName": "Yuxiang Fu",
"affiliation": "School of Electronic Science and Technology, Nanjing University, Nanjing, China",
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"abstract": "Visual querying is essential for interactively exploring massive trajectory data. However, the data uncertainty imposes profound challenges to fulfill advanced analytics requirements. On the one hand, many underlying data does not contain accurate geographic coordinates, e.g., positions of a mobile phone only refer to the regions (i.e., mobile cell stations) in which it resides, instead of accurate GPS coordinates. On the other hand, domain experts and general users prefer a natural way, such as using a natural language sentence, to access and analyze massive movement data. In this paper, we propose a visual analytics approach that can extract spatial-temporal constraints from a textual sentence and support an effective query method over uncertain mobile trajectory data. It is built up on encoding massive, spatially uncertain trajectories by the semantic information of the POIs and regions covered by them, and then storing the trajectory documents in text database with an effective indexing scheme. The visual interface facilitates query condition specification, situation-aware visualization, and semantic exploration of large trajectory data. Usage scenarios on real-world human mobility datasets demonstrate the effectiveness of our approach.",
"abstracts": [
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"content": "Visual querying is essential for interactively exploring massive trajectory data. However, the data uncertainty imposes profound challenges to fulfill advanced analytics requirements. On the one hand, many underlying data does not contain accurate geographic coordinates, e.g., positions of a mobile phone only refer to the regions (i.e., mobile cell stations) in which it resides, instead of accurate GPS coordinates. On the other hand, domain experts and general users prefer a natural way, such as using a natural language sentence, to access and analyze massive movement data. In this paper, we propose a visual analytics approach that can extract spatial-temporal constraints from a textual sentence and support an effective query method over uncertain mobile trajectory data. It is built up on encoding massive, spatially uncertain trajectories by the semantic information of the POIs and regions covered by them, and then storing the trajectory documents in text database with an effective indexing scheme. The visual interface facilitates query condition specification, situation-aware visualization, and semantic exploration of large trajectory data. Usage scenarios on real-world human mobility datasets demonstrate the effectiveness of our approach.",
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"normalizedAbstract": "Visual querying is essential for interactively exploring massive trajectory data. However, the data uncertainty imposes profound challenges to fulfill advanced analytics requirements. On the one hand, many underlying data does not contain accurate geographic coordinates, e.g., positions of a mobile phone only refer to the regions (i.e., mobile cell stations) in which it resides, instead of accurate GPS coordinates. On the other hand, domain experts and general users prefer a natural way, such as using a natural language sentence, to access and analyze massive movement data. In this paper, we propose a visual analytics approach that can extract spatial-temporal constraints from a textual sentence and support an effective query method over uncertain mobile trajectory data. It is built up on encoding massive, spatially uncertain trajectories by the semantic information of the POIs and regions covered by them, and then storing the trajectory documents in text database with an effective indexing scheme. The visual interface facilitates query condition specification, situation-aware visualization, and semantic exploration of large trajectory data. Usage scenarios on real-world human mobility datasets demonstrate the effectiveness of our approach.",
"title": "A Natural-language-based Visual Query Approach of Uncertain Human Trajectories",
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"content": "Visualizations of vascular structures are frequently used in radiological investigations to detect and analyze vascular diseases. Obstructions of the blood flow through a vessel are one of the main interests of physicians, and several methods have been proposed to aid the visual assessment of calcifications on vessel walls. Curved Planar Reformation (CPR) is a wide-spread method that is designed for peripheral arteries which exhibit one dominant direction. To analyze the lumen of arbitrarily oriented vessels, Centerline Reformation (CR) has been proposed. Both methods project the vascular structures into 2D image space in order to reconstruct the vessel lumen. In this paper, we propose Curved Surface Reformation (CSR), a technique that computes the vessel lumen fully in 3D. This offers high-quality interactive visualizations of vessel lumina and does not suffer from problems of earlier methods such as ambiguous visibility cues or premature discretization of centerline data. Our method maintains exact visibility information until the final query of the 3D lumina data. We also present feedback from several domain experts.",
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"abstract": "Parallel clustering is an important research area of big data analysis. The conventional Hierarchical Agglomerative Clustering (HAC) techniques are inadequate to handle big-scale categorical datasets due to two drawbacks. First, HAC consumes excessive CPU time and memory resources; and second, it is non-trivial to decompose clustering tasks into independent sub-tasks executed in parallel. We solve these two problems by a MapReduce-based hierarchical subspace-clustering algorithm - called PAPU - using LSH-based data partitioning. PAPU is conducive to partitioning a large-scale dataset into multiple independent sub-datasets, into which similar data objects are mapped. Advocating parallel computing, PAPU obtains sub-clusters corresponding to respective attribute subspaces from independent chunks in the local clustering phase. To improve the accuracy of approximated clustering results, PAPU measures various scale clusters by applying the hierarchical clustering scheme to iteratively merge sub-clusters during the global clustering phase. We implement PAPU on a 24-node Hadoop computing platform. The experimental results reveal that hierarchical subspace-clustering coupled with the data-partitioning strategy achieves high clustering efficiency on both synthetic and real-world large-scale datasets. The experiments also demonstrate that PAPU delivers superior performance in terms of extensibility and scalability (e.g., a nearly linear speedup).",
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"abstract": "Computer games often take place in extensive virtual worlds, attractive for roaming and exploring. Unfortunately, current virtual cities can strongly hinder this kind of gameplay, since the buildings they feature typically have replicated interiors, or no interiors at all. Procedural content generation is becoming more established, with many techniques for automatically creating specific building elements. However, the integration of these techniques to form complete buildings is still largely unexplored, limiting their application to open game worlds. We propose a novel approach that integrates existing procedural techniques to generate such buildings. With minimal extensions, individual techniques can be coordinated to create buildings with consistently interrelated exteriors and interiors, as in the real world. Our solution offers a framework where various procedural techniques communicate with a moderator, which is responsible for negotiating the placement of building elements, making use of a library of semantic classes and constraints. We demonstrate the applicability of our approach by presenting several examples featuring the integration of a façade shape grammar, two different floor plan layout generation techniques, and furniture placement techniques. We conclude that this approach allows one to preserve the individual qualities of existing procedural techniques, while assisting the consistency maintenance of the generated buildings.",
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"abstract": "We present a multi-GPU design, implementation and performance evaluation of the Halevi-Polyakov-Shoup (HPS) variant of the Fan-Vercauteren (FV) levelled Fully Homomorphic Encryption (FHE) scheme. Our design follows a data parallelism approach and uses partitioning methods to distribute the workload in FV primitives evenly across available GPUs. The design is put to address space and runtime requirements of FHE computations. It is also suitable for distributed-memory architectures, and includes efficient GPU-to-GPU data exchange protocols. Moreover, it is user-friendly as user intervention is not required for task decomposition, scheduling or load balancing. We implement and evaluate the performance of our design on two homogeneous and heterogeneous <inline-formula><tex-math notation=\"LaTeX\">Z_${\\sf NVIDIA}$_Z</tex-math></inline-formula> GPU clusters: <inline-formula><tex-math notation=\"LaTeX\">Z_${\\sf K80}$_Z</tex-math></inline-formula>, and a customized <inline-formula><tex-math notation=\"LaTeX\">Z_${\\sf P100}$_Z</tex-math></inline-formula>. We also provide a comparison with a recent shared-memory-based multi-core CPU implementation using two homomorphic circuits as workloads: vector addition and multiplication. Moreover, we use our multi-GPU Levelled-FHE to implement the inference circuit of two Convolutional Neural Networks (CNNs) to perform homomorphically image classification on encrypted images from the <inline-formula><tex-math notation=\"LaTeX\">Z_${\\sf MNIST}$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_${\\sf CIFAR-10}$_Z</tex-math></inline-formula> datasets. Our implementation provides 1 to 3 orders of magnitude speedup compared with the CPU implementation on vector operations. In terms of scalability, our design shows reasonable scalability curves when the GPUs are fully connected.",
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"content": "We present a multi-GPU design, implementation and performance evaluation of the Halevi-Polyakov-Shoup (HPS) variant of the Fan-Vercauteren (FV) levelled Fully Homomorphic Encryption (FHE) scheme. Our design follows a data parallelism approach and uses partitioning methods to distribute the workload in FV primitives evenly across available GPUs. The design is put to address space and runtime requirements of FHE computations. It is also suitable for distributed-memory architectures, and includes efficient GPU-to-GPU data exchange protocols. Moreover, it is user-friendly as user intervention is not required for task decomposition, scheduling or load balancing. We implement and evaluate the performance of our design on two homogeneous and heterogeneous <inline-formula><tex-math notation=\"LaTeX\">${\\sf NVIDIA}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"sans-serif\">NVIDIA</mml:mi></mml:math><inline-graphic xlink:href=\"qaisarahmadalbadawi-ieq1-3021238.gif\"/></alternatives></inline-formula> GPU clusters: <inline-formula><tex-math notation=\"LaTeX\">${\\sf K80}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"sans-serif\">K</mml:mi><mml:mn mathvariant=\"sans-serif\">80</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"qaisarahmadalbadawi-ieq2-3021238.gif\"/></alternatives></inline-formula>, and a customized <inline-formula><tex-math notation=\"LaTeX\">${\\sf P100}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"sans-serif\">P</mml:mi><mml:mn mathvariant=\"sans-serif\">100</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"qaisarahmadalbadawi-ieq3-3021238.gif\"/></alternatives></inline-formula>. We also provide a comparison with a recent shared-memory-based multi-core CPU implementation using two homomorphic circuits as workloads: vector addition and multiplication. Moreover, we use our multi-GPU Levelled-FHE to implement the inference circuit of two Convolutional Neural Networks (CNNs) to perform homomorphically image classification on encrypted images from the <inline-formula><tex-math notation=\"LaTeX\">${\\sf MNIST}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"sans-serif\">MNIST</mml:mi></mml:math><inline-graphic xlink:href=\"qaisarahmadalbadawi-ieq4-3021238.gif\"/></alternatives></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">${\\sf CIFAR-10}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"sans-serif\">CIFAR</mml:mi><mml:mo>-</mml:mo><mml:mn mathvariant=\"sans-serif\">10</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"qaisarahmadalbadawi-ieq5-3021238.gif\"/></alternatives></inline-formula> datasets. Our implementation provides 1 to 3 orders of magnitude speedup compared with the CPU implementation on vector operations. In terms of scalability, our design shows reasonable scalability curves when the GPUs are fully connected.",
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"title": "Multi-GPU Design and Performance Evaluation of Homomorphic Encryption on GPU Clusters",
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"Homogeneous NVIDIA GPU Clusters",
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"Efficient GPU To GPU Data Exchange Protocols",
"Distributed Memory Architectures",
"FV Primitives",
"Data Parallelism Approach",
"Graphics Processing Units",
"Task Analysis",
"Encryption",
"Computational Modeling",
"Parallel Processing",
"Field Programmable Gate Arrays",
"Homomorphic Encryption",
"Parallel Algorithms",
"Multi GPU Clusters",
"Performance Evaluation"
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{
"givenName": "Ahmad",
"surname": "Al Badawi",
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"affiliation": "Department of Electrical and Computer Engineering, National University of Singapore, Singapore",
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"fullName": "Jie Lin",
"affiliation": "A*STAR, Institute for Infocomm Research (I2R), Singapore",
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"givenName": "Nan",
"surname": "Xiao",
"fullName": "Nan Xiao",
"affiliation": "A*STAR, Institute for Infocomm Research (I2R), Singapore",
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"surname": "Kazuaki",
"fullName": "Matsumura Kazuaki",
"affiliation": "Barcelona Supercomputing Center (BSC), Barcelona, Spain",
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"givenName": "Aung",
"surname": "Khin Mi Mi",
"fullName": "Aung Khin Mi Mi",
"affiliation": "A*STAR, Institute for Infocomm Research (I2R), Singapore",
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"abstract": "Planetary topography is the result of complex interactions between geological processes, of which faulting is a prominent component. Surface-rupturing earthquakes cut and move landforms which develop across active faults, producing characteristic surface displacements across the fault. Geometric models of faults and their associated surface displacements are commonly applied to reconstruct these offsets to enable interpretation of the observed topography. However, current 2D techniques are limited in their capability to convey both the three-dimensional kinematics of faulting and the incremental sequence of events required by a given reconstruction. Here we present a real-time system for interactive retro-deformation of faulted topography to enable reconstruction of fault displacement within a high-resolution (sub 1m/pixel) 3D terrain visualization. We employ geometry shaders on the GPU to intersect the surface mesh with fault-segments interactively specified by the user and transform the resulting surface blocks in realtime according to a kinematic model of fault motion. Our method facilitates a human-in-the-loop approach to reconstruction of fault displacements by providing instant visual feedback while exploring the parameter space. Thus, scientists can evaluate the validity of traditional point-to-point reconstructions by visually examining a smooth interpolation of the displacement in 3D. We show the efficacy of our approach by using it to reconstruct segments of the San Andreas fault, California as well as a graben structure in the Noctis Labyrinthus region on Mars.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Planetary topography is the result of complex interactions between geological processes, of which faulting is a prominent component. Surface-rupturing earthquakes cut and move landforms which develop across active faults, producing characteristic surface displacements across the fault. Geometric models of faults and their associated surface displacements are commonly applied to reconstruct these offsets to enable interpretation of the observed topography. However, current 2D techniques are limited in their capability to convey both the three-dimensional kinematics of faulting and the incremental sequence of events required by a given reconstruction. Here we present a real-time system for interactive retro-deformation of faulted topography to enable reconstruction of fault displacement within a high-resolution (sub 1m/pixel) 3D terrain visualization. We employ geometry shaders on the GPU to intersect the surface mesh with fault-segments interactively specified by the user and transform the resulting surface blocks in realtime according to a kinematic model of fault motion. Our method facilitates a human-in-the-loop approach to reconstruction of fault displacements by providing instant visual feedback while exploring the parameter space. Thus, scientists can evaluate the validity of traditional point-to-point reconstructions by visually examining a smooth interpolation of the displacement in 3D. We show the efficacy of our approach by using it to reconstruct segments of the San Andreas fault, California as well as a graben structure in the Noctis Labyrinthus region on Mars.",
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"normalizedAbstract": "Planetary topography is the result of complex interactions between geological processes, of which faulting is a prominent component. Surface-rupturing earthquakes cut and move landforms which develop across active faults, producing characteristic surface displacements across the fault. Geometric models of faults and their associated surface displacements are commonly applied to reconstruct these offsets to enable interpretation of the observed topography. However, current 2D techniques are limited in their capability to convey both the three-dimensional kinematics of faulting and the incremental sequence of events required by a given reconstruction. Here we present a real-time system for interactive retro-deformation of faulted topography to enable reconstruction of fault displacement within a high-resolution (sub 1m/pixel) 3D terrain visualization. We employ geometry shaders on the GPU to intersect the surface mesh with fault-segments interactively specified by the user and transform the resulting surface blocks in realtime according to a kinematic model of fault motion. Our method facilitates a human-in-the-loop approach to reconstruction of fault displacements by providing instant visual feedback while exploring the parameter space. Thus, scientists can evaluate the validity of traditional point-to-point reconstructions by visually examining a smooth interpolation of the displacement in 3D. We show the efficacy of our approach by using it to reconstruct segments of the San Andreas fault, California as well as a graben structure in the Noctis Labyrinthus region on Mars.",
"title": "Interactive Retro-Deformation of Terrain for Reconstructing 3D Fault Displacements",
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"givenName": "R.",
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"givenName": "T.",
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