Kondo, Masaaki



Faculty of Science and Technology, Department of Information and Computer Science (Yagami)



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Research Areas 【 Display / hide

  • Informatics / Computer system

Research Keywords 【 Display / hide

  • Computer Architecture

  • High Performance Computing


Papers 【 Display / hide

  • A Selective Model Aggregation Approach in Federated Learning for Online Anomaly Detection

    Qin Y., Matsutani H., Kondo M.

    Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020 (Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020)     684 - 691 2020.11

    ISSN  9781728176475

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    Cloud computing has established a convenient approach for computing offloading, where the data produced by edge devices is gathered and processed in a centralized server. However, it results in critical issues related to latency. Recently, a neural network-based on-device learning approach is proposed, which offers a solution to the latency problem by relocating processing data to edge devices; even so, a single edge device may face insufficient training data to train a high-quality model, because of its limited available processing capabilities and energy resources. To address this issue, we extend the work to a federated learning system which enables the edge devices to exchange their trained parameters and update local models. However, in federated learning for anomaly detection, the reliability of local models would be different. For example, a number of trained models are likely to contain the features of anomalous data because of noise corruption or anomaly detection failure. Besides, as the communication protocol amongst edges could be exploited by attackers, the training data or model weights may have potential risks of being poisoned. Therefore, when we design a federated training algorithm, we should carefully select the local models that participate in model aggregation. In this work, we leverage an observed dataset to compute prediction errors, so that the unsatisfying local models can be excluded from federated training. Experimental results show that the federated learning approach improves anomaly detection accuracy. Besides, the proposed model aggregation solution achieves obvious improvement compared with the popular Federated Averaging method.

  • Fast Semi-Supervised Anomaly Detection of Drivers' Behavior using Online Sequential Extreme Learning Machine

    Oikawa H., Nishida T., Sakamoto R., Matsutani H., Kondo M.

    2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 (2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020)   2020.09

    ISSN  9781728141497

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    With the wide spread of artificial intelligence (AI) technologies, many applications using AI are increasingly deployed in many fields. Specially anomaly detection is one of the key applications of AI. Among several targets, detecting anomaly behavior of drivers or vehicles has been attracting due to the growing demand of safety driving. It is crucial to study and evaluate techniques for anomaly driving detection with AI technologies. The Online Sequential Extreme Learning Machine (OS-ELM) is a recently attracting neural network model that has high memory efficiency and can perform highspeed sequential learning with streaming data. Though OSELM is known to be effective for anomaly detection, it has not yet been verified for non-stationary time series data such as driving sensor data. In this paper, we study the effectiveness of OS-ELM based anomaly driving behavior detector using sensor data of vehicles and compared the performance of it with a Hidden Markov Model (HMM) based and traditional Long Short-Term Memory (LSTM) based methods. Since the existing driving behavior benchmark data is not enough for evaluating anomaly driving, we also create a new dataset with a powered wheelchair. Throughout the evaluation, we show that the OS-ELM based anomaly driving detector has almost the same or even better accuracy in anomaly driving detection with much faster sequential learning speed compared with the HMM or LSTM based detector.

  • Energy-efficient on-chip networks through profiled hybrid switching

    He Y., Jiao J., Cao T., Kondo M.

    Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI)     241 - 246 2020.09

    ISSN  9781450379441

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    Virtual channel (VC) flow control is the de facto choice for modern networks-on-chip (NoCs) to allow better utilization of the link bandwidth through buffering and packet switching (PS), which are also the sources of large power footprint and long per-hop latency. However, bandwidth can be plentiful for parallel workloads under VC flow control. Thus, dated but simpler mechanisms, such as circuit switching (CS), can help improve the energy efficiency of modern NoCs. In this paper, we propose to apply CS to part of the link bandwidth so that a considerable amount of traffic can be transmitted bufferlessly without routing. Evaluations reveal that this proposal leads to a reduction of energy per flit by up to 32% while also provides very competitive latency when compared to networks under VC flow control.

  • A neural network-based on-device learning anomaly detector for edge devices

    Tsukada M., Kondo M., Matsutani H.

    IEEE Transactions on Computers (IEEE Transactions on Computers)  69 ( 7 ) 1027 - 1044 2020.07

    ISSN  00189340

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    Semi-supervised anomaly detection is an approach to identify anomalies by learning the distribution of normal data. Backpropagation neural networks (i.e., BP-NNs) based approaches have recently drawn attention because of their good generalization capability. In a typical situation, BP-NN-based models are iteratively optimized in server machines with input data gathered from the edge devices. However, (1) the iterative optimization often requires significant efforts to follow changes in the distribution of normal data (i.e., concept drift), and (2) data transfers between edge and server impose additional latency and energy consumption. To address these issues, we propose ONLAD and its IP core, named ONLAD Core. ONLAD is highly optimized to perform fast sequential learning to follow concept drift in less than one millisecond. ONLAD Core realizes on-device learning for edge devices at low power consumption, which realizes standalone execution where data transfers between edge and server are not required. Experiments show that ONLAD has favorable anomaly detection capability in an environment that simulates concept drift. Evaluations of ONLAD Core confirm that the training latency is 1.95x∼6.58x faster than the other software implementations. Also, the runtime power consumption of ONLAD Core implemented on PYNQ-Z1 board, a small FPGA/CPU SoC platform, is 5.0x∼25.4x lower than them.

  • The Effectiveness of Low-Precision Floating Arithmetic on Numerical Codes: A Case Study on Power Consumption

    Sakamoto R., Kondo M., Fujita K., Ichimura T., Nakajima K.

    ACM International Conference Proceeding Series (ACM International Conference Proceeding Series)     199 - 206 2020.01

    ISSN  9781450372367

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    The low-precision floating point arithmetic that performs computation by reducing numerical accuracy with narrow bit-width is attracting since it can improve the performance of the numerical programs. Small memory footprint, faster computing speed, and energy saving are expected by performing calculation with low precision data. However, there have not been many studies on how low-precision arithmetics affects power and energy consumption of numerical codes. In this study, we investigate the power efficiency improvement by aggressively using low-precision arithmetics for HPC applications. In our evaluations, we analyze power characteristics of the Poisson's equation and the ground motion simulation programs with double precision and single precision floating point arithmetics. We confirm that energy efficiency improves by using low-precision arithmetics but it is heavily influenced by parameters such as data division and the number of OpenMP threads.

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