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Enabling Mixed-Precision Quantized Neural networks in Extreme-Edge Devices 20

Enabling Mixed-Precision Quantized Neural Networks in Extrem...

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17th ACM international conference on computing Frontiers (CF)

作者： Bruschi, Nazareno Garofalo, Angelo Conti, Francesco Tagliavini, Giuseppe Rossi, Davide Univ Bologna Bologna Italy

ISBN: (纸本)9781450379564

the deployment of Quantized Neural networks (QNN) on advanced microcontrollers requires optimized software to exploit digital signal processing (DSP) extensions of modern instruction set architectures (ISA). As such, recent research proposed optimized libraries for QNNs (from 8-bit to 2-bit) such as CMSIS-NN and PULP-NN. this work presents an extension to the PULP-NN library targeting the acceleration of mixed-precision Deep Neural networks, an emerging paradigm able to significantly shrink the memory footprint of deep neural networks with negligible accuracy loss. the library, composed of 27 kernels, one for each permutation of input feature maps, weights, and output feature maps precision (considering 8-bit, 4-bit and 2-bit), enables efficient inference of QNN on parallel ultra-low-power (PULP) clusters of RISC-V based processors, featuring the RV32IMCXpulpV2 ISA. the proposed solution, benchmarked on an 8-cores GAP-8 PULP cluster, reaches peak performance of 16 MACs/cycle on 8 cores, performing 21x to 25x faster than an STM32H7 (powered by an ARM Cortex M7 processor) with 15x to 21x better energy efficiency.

关键词： Embedded Systems Quantized Neural network Low power Architectures

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Q-EEGNet: an Energy-Efficient 8-bit Quantized parallel EEGNet Implementation for Edge Motor-Imagery Brain-Machine Interfaces 6

Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNe...

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6th IEEE international conference on Smart computing (SMARTCOMP)

作者： Schneider, Tibor Wang, Xiaying Hersche, Michael Cavigelli, Lukas Benini, Luca Swiss Fed Inst Technol Dept EE & IT Switzerland Zurich Switzerland Univ Bologna DEI Bologna Italy Huawei Technol Zurich Res Ctr Zurich Switzerland

ISBN: (数字)9781728169972

ISBN: (纸本)9781728169972

Motor-Imagery Brain-Machine Interfaces (MI-BMIs) promise direct and accessible communication between human brains and machines by analyzing brain activities recorded with Electroencephalography (EEG). Latency, reliability, and privacy constraints make it unsuitable to offload the computation to the cloud. Practical use cases demand a wearable, battery-operated device with low average power consumption for long-term use. Recently, sophisticated algorithms, in particular deep learning models, have emerged for classifying EEG signals. While reaching outstanding accuracy, these models often exceed the limitations of edge devices due to their memory and computational requirements. In this paper, we demonstrate algorithmic and implementation optimizations for EEGNET, a compact Convolutional Neural network (CNN) suitable for many BMI paradigms. We quantize weights and activations to 8-bit fixed-point with a negligible accuracy loss of 0.4% on 4-class MI, and present an energy-efficient hardware-aware implementation on the Mr. Wolf parallel ultra-low power (PULP) System-on-Chip (SoC) by utilizing its custom RISC-V ISA extensions and 8-core compute cluster. With our proposed optimization steps, we can obtain an overall speedup of 64 x and a reduction of up to 85% in memory footprint with respect to a single-core layer-wise baseline implementation. Our implementation takes only 5.82 ms and consumes 0.627 mJ per inference. With 21.0 GMAC/s/W, it is 256x more energy-efficient than an EEGNET implementation on an ARM Cortex-M7 (0.082 GMAC/s/W).

关键词： brain-machine interface edge computing parallel computing machine learning deep learning motor imagery

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Deep Learning for Surface Wave Identification in Distributed Acoustic Sensing Data 8

Deep Learning for Surface Wave Identification in Distributed...

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8th IEEE international conference on Big Data (Big Data)

作者： Dumont, Vincent Tribaldos, Veronica Rodriguez Ajo-Franklin, Jonathan Wu, Kesheng Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Rice Univ Houston TX USA

ISBN: (纸本)9781728162515

Moving loads such as cars and trains are very useful sources of seismic waves, which can be analyzed to retrieve information on the seismic velocity of subsurface materials using the techniques of ambient noise seismology. this information is valuable for a variety of applications such as geotechnical characterization of the near-surface, seismic hazard evaluation, and groundwater monitoring. However, for such processes to converge quickly, data segments with appropriate noise energy should be selected. Distributed Acoustic Sensing (DAS) is a novel sensing technique that enables acquisition of these data at very high spatial and temporal resolution for tens of kilometers. One major challenge when utilizing the DAS technology is the large volume of data that is produced, thereby presenting a significant Big Data challenge to find regions of useful energy. In this work, we present a highly scalable and efficient approach to process real, complex DAS data by integrating physics knowledge acquired during a data exploration phase followed by deep supervised learning to identify "useful" coherent surface waves generated by anthropogenic activity, a class of seismic waves that is abundant on these recordings and is useful for geophysical imaging. Data exploration and training were done on 130 Gigabytes (GB) of DAS measurements. Using parallel computing, we were able to do inference on an additional 170 GB of data (or the equivalent of 10 days' worth of recordings) in less than 30 minutes. Our method provides interpretable patterns describing the interaction of ground-based human activities with the buried sensors.

关键词： Big Data Deep Learning Distributed Acoustic Sensing Distributed Training Sensor network Technology

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16th ifip WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2020

16th IFIP WG 12.5 International Conference on Artificial Int...

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16th ifip WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2020

ISBN: (纸本)9783030491604

the proceedings contain 75 papers. the special focus in this conference is on Artificial Intelligence Applications and Innovations. the topics include: Manifold learning for innovation funding: Identification of potential funding recipients;network aggregation to enhance results derived from multiple analytics;PolicyCLOUD: Analytics as a service facilitating efficient data-driven public policy management;demand forecasting of short life cycle products using data mining techniques;Arbitrary scale super-resolution for brain MRI images;knowledge-based fusion for image tampering localization;Transfer learning using convolutional neural network architectures for brain tumor classification from MRI images;a novel learning automata-based strategy to generate melodies from chordal inputs;graph neural networks to advance anticancer drug design;Boosted ensemble learning for anomaly detection in 5G RAN;optimizing self-organizing lists-on-lists using transitivity and pursuit-enhanced object partitioning;task-projected hyperdimensional computing for multi-task learning;cross-domain authorship attribution using pre-trained language models;indoor localization with multi-objective selection of Radiomap models;STDP plasticity in TRN within hierarchical spike timing model of visual information processing;Tensor-based CUDA optimization for ANN inferencing using parallel acceleration on embedded GPU;the random neural network in price predictions;joint multi-object detection and segmentation from an untrimmed video;robust 3D detection in traffic scenario with tracking-based coupling system;Automated MeSH indexing of biomedical literature using contextualized word representations;machine learning for cognitive load classification – A case study on contact-free approach;knowledge-based management and reasoning on cultural and natural touristic routes;ontological foundations of modelling security policies for logical analytics;RDF reasoning on large ontologies: A study on cultural heritage

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An FPGA Accelerator for Bayesian network Structure Learning with Iterative Use of Processing Elements

An FPGA Accelerator for Bayesian Network Structure Learning ...

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19th international conference on Field-Programmable Technology (ICFPT)

作者： Nitta, Yasuhiro Takase, Hideki Kyoto Univ Grad Sch Informat Kyoto Japan JST PRESTO Program Kyoto Japan

ISBN: (纸本)9781665423021

A Bayesian network is one of the graphical models that represent the causality or correlation relationship among multiple observed phenomena. the structure learning of this network is generally NP difficult, and the computational time to obtain an approximate solution becomes huge. this paper proposes an FPGA accelerator for structure learning of Bayesian networks. the proposed method employs a dataflow type architecture and executes processes without dependency in dynamic programming in parallel. By iteratively using processing elements at each processing stage, we can efficiently use limited resources while taking advantage of the parallel performance of FPGAs. We implemented the proposed method for Xilinx Alveo U200 using high-level synthesis. Evaluation results showed that we achieved up to 12.6 times faster than single-core execution of software and up to 2.98 times faster than on multi-core execution. We further applied the proposed method to the Local-to-Global algorithm and achieves 8.6 times faster than the software execution in the structure learning of a practical network with 37 nodes.

关键词： FPGA Bayesian networks reconfigurable computing high-level synthesis codesign

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Scalable Graph Convolutional network based Link Prediction on a Distributed Graph Database Server 13

Scalable Graph Convolutional Network based Link Prediction o...

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IEEE 13th international conference on Cloud computing (CLOUD)

作者： Karunarathna, Anuradha Senarath, Dinika Madhushanki, Shalika Weerakkody, Chinthaka Dayarathna, Miyuru Jayasena, Sanath Suzumura, Toyotaro Univ Moratuwa Dept Comp Sci & Engn Moratuwa Sri Lanka WSO2 Inc Mountain View CA USA IBM TJ Watson Res Ctr New York NY USA MIT IBM Watson AI Lab Cambridge MA USA Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781728187808

Graph Convolutional networks (GCN) have become a popular means of performing link prediction due to the high accuracy offered by them. However, scaling such link prediction into large graphs of billions of vertices and edges with rich types of attributes is a significant issue to be addressed due to the storage and computation limitations of the machines. In this paper we present a scalable link prediction approach which conducts GCN training and link prediction on top of a distributed graph database server called JasmineGraph. We partition graph data and persist them in multiple workers. We implement parallel graph node embedding generation using GraphSAGE algorithm in multiple workers. Our approach avoids facing performance bottlenecks in GCN training using an intelligent scheduling algorithm. We show our approach scales well with an increasing number of partitions (2,4,8, and 16) using four real world data sets called Twitter, Amazon, Reddit, and DBLP-V11. JasmineGraph was able to train a GCN from the largest dataset DBLP-V11 (> 9.3GB) in 11 hours and 40 minutes time using 16 workers on a single server while the original GraphSAGE implementation could not process it at all. the original GraphSAGE implementation processed the second largest dataset Reddit in 238 minutes while JasmineGraph took only 100 minutes on the same hardware with 16 workers leading to 2.4 times improved performance.

关键词： Machine Learning Graph databases Database management Distributed databases Graph theory Graph Convolutional Neural networks Deep learning Link Prediction

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Energy-Efficient Energy Analytics Using a General Purpose Graphics Processing Unit 8

Energy-Efficient Energy Analytics Using a General Purpose Gr...

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8th IEEE international conference on Big Data (Big Data)

作者： De, Sagnik Golab, Wojciech Univ Waterloo Elect & Comp Engn Waterloo ON Canada

ISBN: (纸本)9781728162515

Smart meters allow energy providers to monitor their customers' power consumption. this fine-grained data stream generates many data points, which hides broader trends in power consumption and makes it difficult for energy providers to make decisions regarding a specific customer or a subset of customers. Since the raw power data has little direct use, various algorithms have been proposed to lower the dimensionality of data, discover trends, study relationships between different features of collected data, and summarize data. these analytical techniques make the data more palatable to the end user. Analyzing smart meter data is computationally intensive as there is a large number of households connected to one energy provider, and each household generates years of data at hourly intervals. To speed up the analysis, clusters of commodity computers have been used. Ironically, such clusters consume substantial energy - studies have shown that about 10% of the world-wide supply of electrical power is consumed by the computing infrastructure. In this paper, we describe the use of a graphics processing unit (GPU) to analyze smart meter data, and compare its performance with a conventional multi-core CPU. We discuss the technical challenges in programming a GPU effectively to process smart meter data, and demonstrate experimentally that this choice of implementation enables substantial improvements in terms of both running time and energy-efficiency as compared to the multi-core CPU.

关键词： smart grid smart electricity meters data analytics parallel processing GPU energy-efficiency scalability

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Research on the mac layer performance of wireless and power line parallel communication 8th

Research on the mac layer performance of wireless and power ...

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8th international conference on Computer Engineering and networks, CENet 2018

作者： Liu, Ran Yuan, Jinsha Chen, Zhixiong Liu, Yingchu Han, Dongsheng Guo, Yihe North China Electric Power University BaodingHebei Province071003 China

ISBN: (纸本)9783030146795

Wireless technology is dominant in residential and enterprise networks and it offers mobility and attractive data-rates. Power Line Communication (PLC) is becoming popular in home networks. the main advantages of PLC is the no-new-wires connectivity and the high density of electrical plugs in any residential or enterprise environment. the MAC layer, as one of the keep foundations of networks, is important for improving network performance. However, previous research mainly focused on power line or wireless MAC layer and few studies MAC layer for power line and wireless parallel communication. therefore, this paper proposes a hybrid PLC-Wireless network MAC protocol based on CSMA/CA (carrier sense multiple access with collision avoidance) and we verify its performance via simulation. © Springer Nature Switzerland AG 2020.

关键词： Carrier sense multiple access

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Many Cores and Still Delays - Simulating Multi-Core Communication Software Execution 19

Many Cores and Still Delays - Simulating Multi-Core Communic...

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11th international conference on Computer Modeling and Simulation (ICCMS) / 8th international conference on Intelligent computing and Applications (ICICA)

作者： Dale, Oystein Kristiansen, Stein Plagemann, thomas Peter Volnes, Espen Univ Oslo Dept Informat Oslo Norway

ISBN: (纸本)9781450366199

the effects of software execution are typically ignored in network simulators. this leads to inaccurate simulation results for new and emerging network technologies where packet processing in software has a significant impact on network performance. In this paper we extend our prior work to develop models that capture the temporal aspects of software execution on both uni- and multi-core devices. When executed in parallel with existing network protocol models in network simulators, they significantly improve the accuracy of the simulation results. We demonstrate the applicability of the extended methodology by modeling the execution of communication software on one multi-core device, the Galaxy Nexus smartphone, and evaluate the resulting models in terms of accuracy and scalability. We find that our models are able to reproduce the behavior of the modeled software, and that the models scale well with the number of nodes, cores, and threads included in the simulation;i.e., the simulation overhead increases linearly with the number of nodes.

关键词： network simulation ns-3 communication software execution

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Evaluating POWER Architecture for Distributed Training of Generative Adversarial networks 34th

Evaluating POWER Architecture for Distributed Training of Ge...

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34th international conference on High Performance computing (ISC High Performance)

作者： Hesam, Ahmad Vallecorsa, Sofia Khattak, Gulrukh Carminati, Federico Delft Univ Technol Delft Netherlands CERN Geneva Switzerland

ISBN: (纸本)9783030343569;9783030343552

the increased availability of High-Performance computing resources can enable data scientists to deploy and evaluate data-driven approaches, notably in the field of deep learning, at a rapid pace. As deep neural networks become more complex and are ingesting increasingly larger datasets, it becomes unpractical to perform the training phase on single machine instances due to memory constraints, and extremely long training time. Rather than scaling up, scaling out the computing resources is a productive approach to improve performance. the paradigm of data parallelism allows us to split the training dataset into manageable chunks that can be processed in parallel. In this work, we evaluate the scaling performance of training a 3D generative adversarial network (GAN) on an IBM POWER8 cluster, equipped with 12 NVIDIA P100 GPUs. the full training duration of the GAN, including evaluation, is reduced from 20 h and 16 min on a single GPU, to 2 h and 14min on all 12 GPUs. We achieve a scaling efficiency of 98.9% when scaling from 1 to 12 GPUs, taking only the training process into consideration.

关键词： Distributed training Generative adversarial network High Performance computing GPU POWER8

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