Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions

Deep Learning for 3D Point Clouds: A Survey

Approximate computing trades off computation quality with effort expended, and as rising performance demands confront plateauing resource budgets, approximate computing has become not merely attractive, but even imperative. In this article, we present a survey of techniques for approximate computing (AC). We discuss strategies for finding approximable program portions and monitoring output quality, techniques for using AC in different processing units (e.g., CPU, GPU, and FPGA), processor components, memory technologies, and so forth, as well as programming frameworks for AC. We classify these techniques based on several key characteristics to emphasize their similarities and differences. The aim of this article is to provide insights to researchers into working of AC techniques and inspire more efforts in this area to make AC the mainstream computing approach in future systems.

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A Survey of Techniques for Approximate Computing

Convolutional Neural Networks (CNN) are verycomputation-intensive. Recently, a lot of CNN accelerators based on the CNN intrinsic parallelism are proposed. However, we observed that there is a big mismatch between the parallel types supported by computing engine and the dominant parallel types of CNN workloads. This mismatch seriously degrades resource utilization of existing accelerators. In this paper, we propose aflexible dataflow architecture (FlexFlow) that can leverage the complementary effects among feature map, neuron, and synapse parallelism to mitigate the mismatch. We evaluated our design with six typical practical workloads, it acquires 2-10x performance speedup and 2.5-10x power efficiency improvement compared with three state-of-the-art accelerator architectures. Meanwhile, FlexFlow is highly scalable with growing computing engine scale.

FlexFlow: A Flexible Dataflow Accelerator Architecture for Convolutional Neural Networks

Recent advances in Neural Networks (NN) are enabling more and more innovative applications. As an energy-efficient hardware solution, machine learning accelerators for CNNs or traditional ANNs are also gaining popularity in the area of embedded vision, robotics and cyberphysics. However, the design parameters of NN models vary significantly from application to application. Hence, it's hard to provide one general and highly-efficient hardware solution to accommodate all of them, and it is also impractical for the domain-specific developers to customize their flown hardware targeting on a specific NN model. To deal with this dilemma, this study proposes a design automation tool, DeepBurning, allowing the application developers to build from scratch learning accelerators that targets their specific NN models with custom configurations and optimized performance. DeepBurning includes a RTL-level accelerator generator and a coordinated compiler that generates the control flow and data layout under the user-specified constraints. The results can be used to implement FPGA-based NN accelerator or help generate chip design for early design stage. In general, DeepBurning supports a large family of NN models, and greatly simplifies the design flow of NN accelerators for the machine learning or AI application developers. The evaluation shows that the generated learning accelerators burnt to our FPGA board exhibit great power efficiency compared to state-of-the-art FPGA-based solutions.

DeepBurning: automatic generation of FPGA-based learning accelerators for the neural network family

For autonomous driving, vehicle detection is the prerequisite for many tasks like collision avoidance and path planning. In this letter, we present a real-time three-dimensional (RT3D) vehicle detection method that utilizes pure LiDAR point cloud to predict the location, orientation, and size of vehicles. In contrast to previous 3-D object detection methods, we used a pre-RoIpooling convolution technique that moves a majority of the convolution operations to ahead of the RoI pooling, leaving just a small part behind, so that significantly boosts the computation efficiency. We also propose a pose-sensitive feature map design which can be strongly activated by the relative poses of vehicles, leading to a high regression accuracy on the location, orientation, and size of vehicles. Experiments on the KITTI benchmark dataset show that the RT3D is not only able to deliver competitive detection accuracy against state-of-the-art methods, but also the first LiDAR-based 3-D vehicle detection work that completes detection within 0.09 s which is even shorter than the scan period of mainstream LiDAR sensors.

RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving

Convolutional neural networks (CNN) accelerators have been proposed as an efficient hardware solution for deep learning based applications, which are known to be both compute-and-memory intensive. Although the most advanced CNN accelerators can deliver high computational throughput, the performance is highly unstable. Once changed to accommodate a new network with different parameters like layers and kernel size, the fixed hardware structure, may no longer well match the data flows. Consequently, the accelerator will fail to deliver high performance due to the underutilization of either logic resource or memory bandwidth. To overcome this problem, we proposed a novel deep learning accelerator, which offers multiple types of data-level parallelism: inter-kernel, intra-kernel and hybrid. Our design can adaptively switch among the three types of parallelism and the corresponding data tiling schemes to dynamically match different networks or even different layers of a single network. No matter how we change the hardware configurations or network types, the proposed network mapping strategy ensures the optimal performance and energy-efficiency. Compared with previous state-of-the-art NN accelerators, it is possible to achieve a speedup of 4.0x-8.3x for some layers of the well-known large scale CNNs. For the whole phase of network forward-propagation, our design achieves 28.04% PE energy saving, 90.3% on-chip memory energy saving on average.

C-brain: a deep learning accelerator that tames the diversity of CNNs through adaptive data-level parallelization

Applications' traffic tends to be bursty and the location of hot-spot nodes moves as time goes by. This will significantly aggregate the blocking problem of wormhole-routed Network-on-Chip (NoC). Most of state-of-the-art traffic balancing solutions are based on fully adaptive routing algorithms which may introduce large time/space overhead to routers. Partially adaptive routing algorithms, on the other hand, are time/space efficient, but lack of even or sufficient routing adaptiveness. Reconfigurable routing algorithms could provide on-demand routing adaptiveness for reducing blocking, but most of them are off-line solutions due to the lack of a practical model to dynamically generate deadlock-free routing algorithms. In this paper, we propose the abacus-turn-model (AbTM) for designing time/space-efficient reconfigurable wormhole routing algorithms. Unlike the original turn model, AbTM exploits dynamic communication patterns in applications to reduce the routing latency and chip area requirements. We apply forbidden turns dynamically to preserve deadlock-free operations. Our AbTM routing architecture has two distinct advantages: First, the AbTM leads to a new router architecture without adding virtual channels and routing table. This reconfigurable architecture updates the routing path once the communication pattern changes, and always provides full adaptiveness to hot-spot directions to reduce network blocking. Secondly, the reconfiguration scheme has a good scalability because all operations are carried out between neighbors. We demonstrate these advantages through extensive simulation experiments. The experimental results are indeed encouraging and prove its applicability with scalable performance in large-scale NoC applications.

Xiaowei Li

Papers

FlexFlow: A Flexible Dataflow Accelerator Architecture for Convolutional Neural Networks

DeepBurning: automatic generation of FPGA-based learning accelerators for the neural network family

RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving

C-brain: a deep learning accelerator that tames the diversity of CNNs through adaptive data-level parallelization

An abacus turn model for time/space-efficient reconfigurable routing