Classification of time series data is an important task for many application domains. One of the best existing methods for this task, in terms of accuracy and computation time, is MiniROCKET. In this work, we extend this approach to provide better global temporal encodings using hyperdimensional computing (HDC) mechanisms. HDC (also known as Vector Symbolic Architectures, VSA) is a general method to explicitly represent and process information in high-dimensional vectors. It has previously been used successfully in combination with deep neural networks and other signal processing algorithms. We argue that the internal high-dimensional representation of MiniROCKET is well suited to be complemented by the algebra of HDC. This leads to a more general formulation, HDC-MiniROCKET, where the original algorithm is only a special case. We will discuss and demonstrate that HDC-MiniROCKET can systematically overcome catastrophic failures of MiniROCKET on simple synthetic datasets. These results are confirmed by experiments on the 128 datasets from the UCR time series classification benchmark. The extension with HDC can achieve considerably better results on datasets with high temporal dependence without increasing the computational effort for inference.
Thanks to the tiny storage and efficient execution, hyperdimensional Computing (HDC) is emerging as a lightweight learning framework on resource-constrained hardware. Nonetheless, the existing HDC training relies on various heuristic methods, significantly limiting their inference accuracy. In this paper, we propose a new HDC framework, called LeHDC, which leverages a principled learning approach to improve the model accuracy. Concretely, LeHDC maps the existing HDC framework into an equivalent Binary Neural Network architecture, and employs a corresponding training strategy to minimize the tr…
Hyperdimensional Computing (HDC) has obtained abundant attention as an emerging non von Neumann computing paradigm. Inspired by the way human brain functions, HDC leverages high dimensional patterns to perform learning tasks. Compared to neural networks, HDC has shown advantages such as energy efficiency and smaller model size, but sub-par learning capabilities in sophisticated applications. Recently, researchers have observed when combined with neural network components, HDC can achieve better performance than conventional HDC models. This motivates us to explore the deeper insights behind th…
University of New Mexico（新墨西哥大学）；George Mason University（乔治梅森大学）；Villanova University（维拉诺瓦大学）
This paper represents the first effort to explore an automated architecture search for hyperdimensional computing (HDC), a type of brain-inspired neural network. Currently, HDC design is largely carried out in an application-specific ad-hoc manner, which significantly limits its application. Furthermore, the approach leads to inferior accuracy and efficiency, which suggests that HDC cannot perform competitively against deep neural networks. Herein, we present a thorough study to formulate an HDC architecture search space. On top of the search space, we apply reinforcement-learning to automatic…
Inspired by the way human brain works, the emerging hyperdimensional computing (HDC) is getting more and more attention. HDC is an emerging computing scheme based on the working mechanism of brain that computes with deep and abstract patterns of neural activity instead of actual numbers. Compared with traditional ML algorithms such as DNN, HDC is more memory-centric, granting it advantages such as relatively smaller model size, less computation cost, and one-shot learning, making it a promising candidate in low-cost computing platforms. However, the robustness of HDC models have not been syste…
Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning (ML) methods. It is based on large vectors of binary or bipolar symbols and a few simple mathematical operations. The promise of HDC is a highly efficient implementation for embedded systems like wearables. While fast implementations have been presented, other constraints have not been considered for edge computing. In this work, we aim at answering how thermal-friendly HDC for edge computing is. Devices like smartwatches, smart glasses, or even mobile systems have a restrictive cooling budget due to their limited …
Hyperdimensional Computing (HDC) developed by Kanerva is a computational model for machine learning inspired by neuroscience. HDC exploits characteristics of biological neural systems such as high-dimensionality, randomness and a holographic representation of information to achieve a good balance between accuracy, efficiency and robustness. HDC models have already been proven to be useful in different learning applications, especially in resource-limited settings such as the increasingly popular Internet of Things (IoT). One class of learning tasks that is missing from the current body of work…
Hyperdimensional computing (HDC) is an emerging learning paradigm that computes with high dimensional binary vectors. It is attractive because of its energy efficiency and low latency, especially on emerging hardware -- but HDC suffers from low model accuracy, with little theoretical understanding of what limits its performance. We propose a new theoretical analysis of the limits of HDC via a consideration of what similarity matrices can be "expressed" by binary vectors, and we show how the limits of HDC can be approached using random Fourier features (RFF). We extend our analysis to the more …
Being an emerging class of in-memory computing architecture, brain-inspired hyperdimensional computing (HDC) mimics brain cognition and leverages random hypervectors (i.e., vectors with a dimensionality of thousands or even more) to represent features and to perform classification tasks. The unique hypervector representation enables HDC classifiers to exhibit high energy efficiency, low inference latency and strong robustness against hardware-induced bit errors. Consequently, they have been increasingly recognized as an appealing alternative to or even replacement of traditional deep neural ne…
A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations
University of California, Berkeley（加州大学伯克利分校）；Research Institutes of Sweden（瑞典研究所）；International Research and Training Center for Information Technologies and Systems（国际信息技术与系统研究培训中心）；Luleå University of Technology（吕勒奥理工大学）；IBM Research（IBM研究院）
Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience. HDC represents data as high-dimensional, low-precision vectors which can be used for a variety of information processing tasks like learning or recall. The mapping to high-dimensional space is a fundamental problem in HDC, and existing methods encounter scalability issues when the input data itself is high-dimensional. In this work, we explore a family of streaming encoding techniques based on hashing. We show formally that these methods enjoy comparable guarantees on …
Hyperdimensional Computing (HDC) is a computation framework based on properties of high-dimensional random spaces. It is particularly useful for machine learning in resource-constrained environments, such as embedded systems and IoT, as it achieves a good balance between accuracy, efficiency and robustness. The mapping of information to the hyperspace, named encoding, is the most important stage in HDC. At its heart are basis-hypervectors, responsible for representing the smallest units of meaningful information. In this work we present a detailed study on basis-hypervector sets, which leads t…
Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction
School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut（康奈尔大学机械、航空航天与制造工程学院）；U.S. Army Combat Capabilities Development Command, Ground Vehicle Systems Center（美国陆军战斗能力发展指挥部地面车辆系统中心）
MissionHD: Hyperdimensional Refinement of Distribution-Deficient Reasoning Graphs for Video Anomaly Detection
Department of Computer Science University of California, Irvine（计算机科学系加州大学伊维奇分校）；Department of Electrical Engineering & Computer Science United States Military Academy, West Point（电气工程与计算机科学系美国军事学院西点分校）
VS-Graph: Scalable and Efficient Graph Classification Using Hyperdimensional Computing
Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA（电气与计算机工程系，乔治·马歇尔大学，弗吉尼亚州 Fairfax）；Oak Ridge National Laboratory, Oak Ridge, TN, USA（橡树岭国家实验室，橡树岭，田纳西州）
Hyperdimensional Computing for Sustainable Manufacturing: An Initial Assessment
School of Mechanical, Aerospace, and Manufacturing Engineering University of Connecticut Storrs, Connecticut, USA（机械、航空航天与制造工程学院 美国康涅狄格大学 斯托尔斯分校）；Department of Mechanical and Aerospace Engineering Rutgers University New Brunswick, NJ, USA（机械与航空航天工程系 罗格斯大学 新泽西州新布朗斯维爾分校）
Towards Efficient Hyperdimensional Computing Using Photonics
Boston University（波士顿大学）；University of California, Irvine（加利福尼亚大学欧文分校）；Georgia Institute of Technology（佐治亚理工学院）；SRI International（SRI国际）；Lightmatter（莱特马特公司）
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Siemens Corporate Technology（西门子公司企业技术部）
Hyperdimensional computing (HDC) has emerged as a new light-weight learning algorithm with smaller computation and energy requirements compared to conventional techniques. In HDC, data points are represented by high-dimensional vectors (hypervectors), which are mapped to high-dimensional space (hyperspace). Typically, a large hypervector dimension ($\geq1000$) is required to achieve accuracies comparable to conventional alternatives. However, unnecessarily large hypervectors increase hardware and energy costs, which can undermine their benefits. This paper presents a technique to minimize the …
Domain-Aware Hyperdimensional Computing for Edge Smart Manufacturing
School of Mechanical, Aerospace, and Manufacturing Engineering University of Connecticut（机械、航空航天与制造工程学院 美国康涅狄格大学）；Department of Mechanical and Aerospace Engineering Rutgers University（机械与航空航天工程系 罗格斯大学）
University of California San Diego（加利福尼亚大学圣地亚哥分校）；University of California Los Angeles（加利福尼亚大学洛杉矶分校）；University of Connecticut（康涅狄格大学）；Daegu Gyeongbuk Institute of Science and Technology（大邱庆北科学技术院）；University of California Irvine（加利福尼亚大学欧文分校）
Recently, brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown promising results in enabling efficient and robust cognitive learning. Despite the success, these two brain-inspired models have different strengths. While SNN mimics the physical properties of the human brain, HDC models the brain on a more abstract and functional level. Their design philosophies demonstrate complementary patterns that motivat…
Health monitoring applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings. Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time. However, resource constraints on most of these wearable devices prevent the ability to perform online learning on them. To address this issue, it is required to rethink the machine learning mo…
Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD
Computer Science and Engineering, Wright State University, Ohio, USA（计算机科学与工程系，怀特州立大学，俄亥俄州，美国）；Computer Science and Engineering, Azad University, Ahvaz, Iran（计算机科学与工程系，阿扎德大学，阿瓦兹，伊朗）
Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset
Computer Science and Engineering, Wright State University, Ohio, USA（计算机科学与工程系，怀特州立大学，俄亥俄州，美国）；Computer Science and Engineering, Azad University, Ahvaz, Iran（计算机科学与工程系，阿扎德大学，阿瓦兹，伊朗）
