論文

In the real world, normal and abnormal behavior patterns vary depending on a given environment, which means that the abnormal behavior detection model should be customized. To address this issue, in this paper, we employ OS-ELM (Online Sequential Extreme Learning Machine) and Autoencoder for adaptive abnormal behavior detection. First, state-transition probability tables of a target during an initial learning period are learned as normal behaviors. Then, Autoencoder-based anomaly detection is performed for the state-transition probability tables of subsequent time frames. The abnormal behavior detection model is updated by using OS-ELM algorithm every time a new probability table or behavior comes. The number of abnormal behavior detection instances is dynamically tuned to reflect the recent normal patterns or modes. Also, the table is compressed to reduce the computation cost. Evaluation results using a driving dataset of cars show that the proposed abnormal behavior detection accurately identifies normal and aggressive driving patterns with the optimal number of the abnormal behavior detection instances.

Toward on-device anomaly detection for time-series data, in this paper, we analyze Echo State Network (ESN), which is a simple form of Recurrent Neural Networks (RNNs), and propose its area-efficient implementation. It is evaluated in terms of the anomaly detection capability and area. (Keywords: On-device learning, Machine learning, and Anomaly detection)

Since most sensor data depend on each other, time-series anomaly detection is one of practical applications of IoT devices. Such tasks are handled by Recurrent Neural Networks (RNNs) with a feedback structure, such as Long Short Term Memory. However, their learning phase based on Stochastic Gradient Descent (SGD) is computationally expensive for such edge devices. This issue is addressed by executing their learning on high-performance server machines, but it introduces a communication overhead and additional power consumption. On the other hand, Recursive Least-Squares Echo State Network (RLS-ESN) is a simple RNN that can be trained at low cost using the least-squares method rather than SGD. In this paper, we propose its area-efficient hardware implementation for edge devices and adapt it to human activity anomaly detection as an example of interdependent time-series sensor data. The model is implemented in Verilog HDL, synthesized with a 45 nm process technology, and evaluated in terms of the anomaly capability, hardware amount, and performance. The evaluation results demonstrate that the RLS-ESN core with a feedback structure is more robust to hyper parameters than an existing Online Sequential Extreme Learning Machine (OS-ELM) core. It consumes only 1.25 times larger hardware amount and 1.11 times longer latency than the existing OS-ELM core.

R-GCN (Relational Graph Convolutional Network) is one of GNNs (Graph Neural Networks). The model tries predicting latent information by considering directions and types of edges in graph-structured data, such as knowledge bases. The model builds weight matrices to each edge attribute. Thus, the size of the neural network increases linearly with the number of edge types. Although GPUs can be used for accelerating the R-GCN processing, there is a possibility that the size of weight matrices exceeds GPU device memory. To address this issue, in this paper, an edge attribute-wise partitioning is proposed for R-GCN. The proposed partitioning divides the model and graph data so that R-GCN can be accelerated by using multiple GPUs. Also, the proposed approach can be applied to sequential execution on a single GPU. Both the cases can accelerate the R-GCN processing with large graph data, where the original model cannot be fit into a device memory of a single GPU without partitioning. Experimental results demonstrate that our partitioning method accelerates R-GCN by up to 3.28 times using four GPUs compared to CPU execution for a dataset with more than 1.6 million nodes and 5 million edges. Also, the proposed approach can accelerate the execution even with a single GPU by 1.55 times compared to the CPU execution for a dataset with 0.8 million nodes and 2 million edges.

An efficient hardware implementation for Simultaneous Localization and Mapping (SLAM) methods is of necessity for mobile autonomous robots with limited computational resources. In this paper, we propose a resource-efficient FPGA implementation for accelerating scan matching computations, which typically cause a major bottleneck in 2D LiDAR SLAM methods. Scan matching is a process of correcting a robot pose by aligning the latest LiDAR measurements with an occupancy grid map, which encodes the information about the surrounding environment. We exploit an inherent parallelism in the Rao-Blackwellized Particle Filter (RBPF) based algorithm to perform scan matching computations for multiple particles in parallel. In the proposed design, several techniques are employed to reduce the resource utilization and to achieve the maximum throughput. Experimental results using the benchmark datasets show that the scan matching is accelerated by 5.31-8.75× and the overall throughput is improved by 3.72-5.10× without seriously degrading the quality of the final outputs. Furthermore, our proposed IP core requires only 44% of the total resources available in the TUL Pynq-Z2 FPGA board, thus facilitating the realization of SLAM applications on indoor mobile robots.

In statistical analysis and data mining, change-point detection that identifies the change-points which are times when the probability distribution of time series changes has been used for various purposes, such as anomaly detections on network traffic and transaction data. However, computation cost of a conventional AR (Auto-Regression) model based approach is too high and infeasible for online. In this paper, an AR model based online change-point detection algorithm, called ChangeFinder, is implemented on an FPGA (Field Programmable Gate Array) based NIC (Network Interface Card). The proposed system computes the change-point score from time series data received from 10GbE (10Gbit Ethernet). More specifically, it computes the change-point score at the 10GbE NIC in advance of host applications. It can find change-points on single or multiple streams using a context memory. This paper aims to reduce the host workload and improve change-point detection performance by offloading ChangeFinder algorithm from host to the NIC. As evaluations, change-point detection in the FPGA NIC is compared with a baseline software implementation and those enhanced by two network optimization techniques using DPDK and Netfilter in terms of throughput. The result demonstrates 16.8x improvement in change-point detection throughput compared to the baseline software implementation. It is corresponding to the 10GbE line rate. Performance and area overheads when supporting multiple streams are also evaluated.

DQN (Deep Q-Network) is a method to perform Q-learning for reinforcement learning using deep neural networks. DQNS require a large buffer and batch processing for an experience replay and rely on a backpropagation based iterative optimization, making them difficult to be implemented on resource-limited edge devices. In this paper, we propose a lightweight on-device reinforcement learning approach for low-cost FPGA devices. It exploits a recently proposed neural-network based on-device learning approach that does not rely on the backpropagation method but uses OS-ELM (Online Sequential Extreme Learning Machine) based training algorithm. In addition, we propose a combination of L2 regularization and spectral normalization for the on-device reinforcement learning so that output values of the neural network can be fit into a certain range and the reinforcement learning becomes stable. The proposed reinforcement learning approach is designed for PYNQ-Z1 board as a low-cost FPGA platform. The evaluation results using OpenAI Gym demonstrate that the proposed algorithm and its FPGA implementation complete a CartPole-v0 task 29.77x and 89.40x faster than a conventional DQN-based approach when the number of hidden-layer nodes is 64.

In distributed deep neural network using remote GPU nodes, communication occurs iteratively between remote nodes for gradient aggregation. This communication latency limits the benefit of distributed training with faster GPUs. In distributed deep learning using the remote GPUs, workload of gradient aggregation is imposed on a host machine. In this paper, we therefore propose to offload the gradient aggregation to a DPDK (Data Plane Development Kit) based network switch between the host machine and remote GPUs. In this approach, the aggregation process is completed in the network using extra computation resources in the network switch. We evaluate the proposed switch when GPUs and the host communicate with a standard IP communication and a PCI Express (PCIe) over 40Gbit Ethernet (40GbE) product, respectively. The evaluation results using a standard IP communication show that the aggregation is accelerated by 2.2-2.5x compared to the aggregation executed by a host machine. The results using the PCIe over 40GbE product show that the proposed switch outperforms the aggregation done by the host machine by 1.16x. This approach is thus useful for distributed training with multiple GPUs.

Most edge AI focuses on prediction tasks on resource-limited edge devices while the training is done at server machines. However, retraining or customizing a model is required at edge devices as the model is becoming outdated due to environmental changes over time. To follow such a concept drift, a neural-network based on-device learning approach is recently proposed, so that edge devices train incoming data at runtime to update their model. In this case, since a training is done at distributed edge devices, the issue is that only a limited amount of training data can be used for each edge device. To address this issue, one approach is a cooperative learning or federated learning, where edge devices exchange their trained results and update their model by using those collected from the other devices. In this paper, as an on-device learning algorithm, we focus on OS-ELM (Online Sequential Extreme Learning Machine) to sequentially train a model based on recent samples and combine it with autoencoder for anomaly detection. We extend it for an on-device federated learning so that edge devices can exchange their trained results and update their model by using those collected from the other edge devices. This cooperative model update is one-shot while it can be repeatedly applied to synchronize their model. Our approach is evaluated with anomaly detection tasks generated from a driving dataset of cars, a human activity dataset, and MNIST dataset. The results demonstrate that the proposed on-device federated learning can produce a merged model by integrating trained results from multiple edge devices as accurately as traditional backpropagation based neural networks and a traditional federated learning approach with lower computation or communication cost.

The performance prediction for a NoC-based Chip Multi-Processor (CMP) is one of the main design concerns. Generally, there is a trade-off between accuracy and time overhead on the performance prediction of computer systems. In particular, the time overhead is proportional or exponential to the number of cores when using a cycle-accurate full-system simulation, such as gem5. In this study, we propose an accurate and scalable method to predict the influence of design NoC parameters on its performance. Our method counts the number of execution cycles when employing the target NoC based on the statistics of one-time execution of a full-system simulation using a fully-connected NoC. To evaluate the accuracy and execution time overhead, we use the case that randomly generates allocations of processors with 3D mesh topology NoC. Its Mean Absolute Percentage Error of the estimated cycles is about 4.7%, and the Maximum Absolute Percentage Error is about 8.5%.

A building block computing system is consisting of multiple chips connecting with inductive coupling wireless through chip interconnect. Like building Lego blocks, various types of systems can be built by stacking different types of chips. In order to develop such systems, several techniques are investigated in the project: that is, inductive coupling wireless through chip interface, low power circuit technologies, autonomous interconnection network architectures, thermal dissipation and building block operating system. Here, the overview of the project and a prototype system are introduced.

End-to-end network latency has become an important issue for big data processing on large-scale and heterogeneous data centers. To realize them, a low-latency inter-switch network is required. In recent years, a novel network based on a random regular graph such as Jellyfish has been proposed as a solution to these problems. To realize this network on Infiniband, we explore layered shortest path routing that guarantees deadlock freedom. Infiniband can avoid deadlocks by using Virtual Lane (VL), which is a QoS guarantee mechanism. However, the output VL is indirectly determined by using Service Level (SL) held by each packet. In this research, to minimize the number of consumed SLs, we propose an algorithm based on a graph coloring problem to assign SLs to all source-destination pairs. The proposed method can reduce the number of consumed SLs by up to 38%compared to the conventional SL mapping method.

A host-switch graph was originally proposed as a graph that represents a network topology of a computer systems with 1-port host computers and (Formula presented.) -port switches. It has been studied from both theoretical and practical aspects in terms of the diameter, the average shortest path length, and the performance of real applications. In recent high-performance computing systems, however, a host computer is connected to multiple switches by using InfiniBand, NVSwitch, or Omni-Path, and consequently they provide high bandwidths. Since a host-switch graph cannot represent such systems, this article extends a host-switch graph so that it can represent such systems. As a result, a host-switch graph can include multi-ported hosts. Furthermore, we propose to use multi-port hosts for reducing the diameter. We show that the diameter minimization is equivalent to solving the degree diameter problem for bipartite graphs of diameter three. Our experimental results show that we can drastically reduce the diameter as well as increasing the bandwidth and improves performance of MPI applications by up to 162% as compared with networks with single-ported hosts.

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.

Blockchain is a distributed ledger system composed of a P2P network and is used for a wide range of applications, such as international remittance, inter-individual transactions, and asset conservation. In Blockchain systems, tamper resistance is enhanced by the property of transaction that cannot be changed or deleted by everyone including the creator of the transaction. However, this property also becomes a problem that unintended transaction created by miss operation or secret key theft cannot be corrected later. Due to this problem, once an illegal transaction such as theft occurs, the damage will expand. To suppress the damage, we need countermeasures, such as detecting illegal transaction at high speed and correcting the transaction before approval. However, anomaly detection in the Blockchain at high speed is computationally heavy, because we need to repeat the detection process using various feature quantities and the feature extractions become overhead. In this paper, to accelerate anomaly detection, we propose to cache transaction information necessary for extracting feature in GPU device memory and perform both feature extraction and anomaly detection in the GPU. We also propose a conditional feature extraction method to reduce computation cost of anomaly detection. We employ anomaly detection using K-means algorithm based on the conditional features. When the number of users is one million and the number of transactions is 100 millions, our proposed method achieves 8.6 times faster than CPU processing method and 2.6 times faster than GPU processing method that does not perform feature extraction on the GPU. In addition, the conditional feature extraction method achieves 1.7 times faster than the unconditional method when the number of users satisfying a given condition is 200 thousands out of one million.

Network throughput has become an important issue for big-data analysis on Warehouse-Scale Computing (WSC) systems. It has been reported that randomly-connected inter-switch networks can enlarge the network throughput. For irregular networks, a multi-path routing method called k-shortest path routing is conventionally utilized. However, it cannot efficiently exploit longer-than-shortest paths that would be detour paths to avoid bottlenecks. In this work, a novel routing method called k-optimized path routing to achieve high throughput is proposed for irregular networks. We introduce a heuristic to select detour paths that can avoid bottlenecks in the network to improve the network throughput. Experimental results show that the proposed k-optimized path routing can improve the throughput by up to 133 % compared to the conventional k-shortest path routing. Moreover, it can improve the network bandwidth while maintaining the low network latency.

Low-power embedded systems often require simple but flexible functionality for data management. Key-value store is one of data stores, which provides simple API and is easy to scale-out. We developed a dedicated key-value store core as a low-power embedded storage. In this paper, key-value store chip is fabricated with Renesas SOTB 65-nm process technology. We evalute the real chip in terms of power consumption and operating frequency by tuning VDD and body bias voltage. The chip achieved 11.2mW as power consumption with 0.7V VDD if the target frequency is 40MHz. When data rate is low, the system can reduce power consumption by tuning VDD and body bias voltage.

In-network computing accelerates applications natively running on the host by executing them within network devices. While in-network computing offers significant performance improvements, its limitations and design trade-offs have not been explored. To usefully and efficiently run applications within the network, we first need to understand the implications of their design. In this work we introduce LaKe, a Layered Key-Value Store design, running as an in-network application. LaKe is a scalable design, enabling the exploration of design decisions and their effect on throughput, latency and power efficiency. LaKe achieves full line rate throughput, while maintaining a latency of 1.1 μs and better power efficiency than existing hardware based memcached designs.

As the scale of supercomputers has increased, the end-to-end network latency has become non-negligible on the performance of parallel applications. For this reason, low-latency network topologies achieving the smallest number of hops among switches are drawing attention. The Order/Degree Problem to find the topology with the smallest diameter has revealed that a topology based on graph symmetry and simulated annealing (SA) is optimal for a wide range of system sizes. Focusing on the characteristics of clustering and symmetry of this graph, we extend the method to guarantee the lower limit of the number of cables in each cabinet. The total cable length and cost can be minimized as well as minimizing the deterioration of communication delay. Compared with the conventional network topologies, the proposed network topology can attain higher performance with the smaller total cable length and cost.

Three-dimensional stack memory that provides both high-bandwidth access and large capacity is a promising technology for next-generation computer systems. While a large number of memory cubes increase the aggregate memory capacity, the communication latency and power consumption would become significant due to its low-radix large-diameter packet network. In this context, we propose a memory-cube network called Diagonal Memory Network (DMN). A diagonal network topology, its floor layout, and its lightweight router are designed for low-latency and low-voltage memory-read communication. Our evaluation results show that a DMN router decreases 31% of the hardware resources than a conventional virtual-channel router. The DMN router reduces 13% and 67% energy consumption to transit a packet along with the original datapath and bypassing datapath, respectively.