Uncertainty Quantification (UQ) is crucial for ensuring the reliability of machine learning models deployed in real-world autonomous systems. However, existing approaches typically quantify task-level output prediction uncertainty without considering epistemic uncertainty at the multimodal feature fusion level, leading to sub-optimal outcomes. Additionally, popular uncertainty quantification methods, e.g., Bayesian approximations, remain challenging to deploy in practice due to high computational costs in training and inference. In this paper, we propose HyperDUM, a novel deterministic uncertainty method (DUM) that efficiently quantifies feature-level epistemic uncertainty by leveraging hyper-dimensional computing. Our method captures the channel and spatial uncertainties through channel and patch -wise projection and bundling techniques respectively. Multimodal sensor features are then adaptively weighted to mitigate uncertainty propagation and improve feature fusion. Our evaluations show that HyperDUM on average outperforms the state-of-the-art (SOTA) algorithms by up to 2.01%/1.27% in 3D Object Detection and up to 1.29% improvement over baselines in semantic segmentation tasks under various types of uncertainties. Notably, HyperDUM requires 2.36× less Floating Point Operations and up to 38.30× less parameters than SOTA methods, providing an efficient solution for real-world autonomous systems.

Deep learning has been widely adopted for human activity recognition (HAR) while generalizing a trained model across diverse users and scenarios remains challenging due to distribution shifts (DS). The inherent low-resource challenge in HAR, i.e., collecting and labeling adequate human-involved data can be prohibitively costly, further raising the difficulty of tackling DS. We propose TACO, a novel transformer-based contrastive meta-learning approach for generalizable HAR. TACO addresses DS by synthesizing virtual target domains in training with explicit consideration of model generalizability. Additionally, we extract expressive feature with the attention mechanism of Transformer and incorporate the supervised contrastive loss function within our meta-optimization to enhance representation learning. Our evaluation demonstrates that TACO achieves notably better performance across various low-resource DS scenarios.

Due to the rapidly evolving landscape of cybersecurity, the risks in securing cloud networks and devices are attesting to be an increasingly prevalent research challenge. Reinforcement learning (RL) is a subfield of machine learning that has demonstrated its ability to detect cyberattacks, as well as its potential to recognize new ones. Many of the popular RL algorithms at present rely on deep neural networks, which are computationally very expensive to train. An alternative approach to this class of algorithms is hyperdimensional computing (HDC), which is a robust, computationally efficient learning paradigm that is ideal for powering resource-constrained devices. In this article, we present CyberRL, a HDC algorithm for learning cybersecurity strategies for intrusion detection in an abstract Markov game environment. We demonstrate that CyberRL is advantageous compared to its deep learning equivalent in computational efficiency, reaching up to$1.9{\times }$speedup in training time for multiple devices, including low-powered devices. We also present its enhanced learning quality and superior defense and attack security strategies with up to$12.8\times $improvement. We implement our framework on Xilinx Alveo U50 FPGA and achieve approximately$700\times $speedup and energy efficiency improvements compared to the CPU execution.

Many real-world applications of the Internet of Things (IoT) employ machine learning (ML) algorithms to analyze time series information collected by interconnected sensors. However, distribution shift, a fundamental challenge in data-driven ML, arises when a model is deployed on a data distribution different from the training data and can substantially degrade model performance. Additionally, increasingly sophisticated deep neural networks (DNNs) are required to capture intricate spatial and temporal dependencies in multi-sensor time series data, often exceeding the capabilities of today's edge devices. In this paper, we propose SMORE, a novel resource-efficient domain adaptation (DA) algorithm for multi-sensor time series classification, leveraging the efficient and parallel operations of hyperdimensional computing. SMORE dynamically customizes test-time models with explicit consideration of the domain context of each sample to mitigate the negative impacts of domain shifts. Our evaluation on a variety of multi-sensor time series classification tasks shows that SMORE achieves on average 1.98% higher accuracy than state-of-the-art (SOTA) DNN-based DA algorithms with 18.81x faster training and 4.63x faster inference.

Effectively capturing intricate interactions among road users plays a critical role in achieving safe navigation for autonomous vehicles. While graph learning (GL) has emerged as a promising approach to tackle this challenge, existing GL models rely on predefined domain-specific graph extraction rules and often fail in real-world dynamic scenarios. Additionally, these graph extraction rules severely impede the capability of existing GL methods to generalize knowledge across domains. To address this issue, we propose RoadScene2Graph (RS2G), an innovative autonomous scenario understanding framework with a novel data-driven graph extraction and modeling approach that dynamically captures the diverse relations among road users. Our evaluations show that on average RS2G outperforms the state-of-the-art (SOTA) rule-based graph extraction method by 4.47% and the SOTA deep learning model by 22.19% in subjective risk assessment. RS2G also delivers notably better performance in transferring knowledge gained from simulations to unseen real-world scenarios.

Network-based security has emerged as an increasingly critical challenge in the domain of the Internet of Things (IoT). A number of network intrusion detection systems (NIDS), typically relying on sophisticated machine learning (ML) algorithms, have been proposed to monitor network traffic and detect malicious activity. However, these NIDS designs require extensive memory and computational power, exceeding the capability of today’s IoT devices, and often fail to provide timely detection of network attacks. To tackle this issue, we propose HyperDetect, the first attempt at NIDS modeling that leverages the highly efficient and parallel operations of brain-inspired hyperdimensional computing (HDC). Our innovative model updating method effectively mitigates model saturation and significantly reduces the number of retraining iterations needed to reach convergence. Additionally, we employ a novel dynamic encoding technique to regenerate insignificant dimensions, considerably lowering the dimensionalities required to achieve high-quality performance and further accelerating the learning process. HyperDetect delivers on average 5.02× faster training and 31.83× faster inference compared to state-of-the-art (SOTA) learning approaches on a wide range of network intrusion classification tasks. We also extensively evaluate HyperDetect on embedded hardware to demonstrate its low-latency and resource-efficient characteristics.

Cybersecurity has emerged as a critical challenge for the industry. With the large complexity of the security landscape, sophisticated and costly deep learning models often fail to provide timely detection of cyber threats on edge devices. Brain-inspired hyperdimensional computing (HDC) has been introduced as a promising solution to address this issue. However, existing HDC approaches use static encoders and require very high dimensionality and hundreds of training iterations to achieve reasonable accuracy. This results in a serious loss of learning efficiency and causes huge latency for detecting attacks. In this paper, we propose CyberHD, an innovative HDC learning framework that identifies and regenerates insignificant dimensions to capture complicated patterns of cyber threats with remarkably lower dimensionality. Additionally, the holographic distribution of patterns in high dimensional space provides CyberHD with notably high robustness against hardware errors.

The Internet of Things (IoT) has become an emerging trend that connects heterogeneous devices and enables them with new capabilities. Many applications exploit machine learning methodology to dissect collected data, and edge computing was introduced to enhance the efficiency and scalability in resource-constrained computing environments. Unfortunately, popular deep learning algorithms involve intensive computations that are overcomplicated for edge devices. Brain-inspired Hyperdimensional Computing (HDC) has been considered a promising approach to address this issue. However, existing HDC methods use static encoders, and thus require extremely high dimensionality and hundreds of training iterations to achieve reasonable accuracy. This results in a huge loss of efficiency and severely impedes the application of HDC algorithms in power-limited machines. In this paper, we propose DistHD, a novel HDC framework with a unique dynamic encoding technique consisting of two parts: top-2 classification and dimension regeneration. Our top-2 classification provides top-2 labels for each data sample based on cosine similarity, and dimension regeneration identifies and regenerates dimensions that mislead the classification and reduce the learning quality. The highly parallel algorithm of DistHD effectively accelerates the learning process and achieves the desired accuracy with considerably lower dimensionality. Our evaluation on a wide range of practical classification tasks shows that DistHD is capable of achieving on average 2.12% higher accuracy than state-of-the-art (SOTA) HDC approaches while reducing dimensionality by 8.0×. It delivers 5.97× faster training and 8.09× faster inference than SOTA learning algorithms. Additionally, the holographic distribution of patterns in high dimensional space provides DistHD with 12.90× higher robustness against hardware errors than SOTA DNNs. DistHD has been open-sourced to enable future research in this field.1

With the rapid evolution of the Internet of Things, many real-world applications utilize heterogeneously connected sensors to capture time-series information. Edge-based machine learning (ML) methodologies are often employed to analyze locally collected data. However, a fundamental issue across data-driven ML approaches is distribution shift. It occurs when a model is deployed on a data distribution different from what it was trained on, and can substantially degrade model performance. Additionally, increasingly sophisticated deep neural networks (DNNs) have been proposed to capture spatial and temporal dependencies in multi-sensor time series data, requiring intensive computational resources beyond the capacity of today's edge devices. While brain-inspired hyperdimensional computing (HDC) has been introduced as a lightweight solution for edge-based learning, existing HDCs are also vulnerable to the distribution shift challenge. In this paper, we propose DOMINO, a novel HDC learning framework addressing the distribution shift problem in noisy multi-sensor time-series data. DOMINO leverages efficient and parallel matrix operations on high-dimensional space to dynamically identify and filter out domain-variant dimensions. Our evaluation on a wide range of multi-sensor time series classification tasks shows that DOMINO achieves on average 2.04% higher accuracy than state-of-the-art (SOTA) DNN-based domain generalization techniques, and delivers$16.34\times$faster training and$2.89\times$faster inference. More importantly, DOMINO exhibits notably better performance when learning from partially labeled data and highly imbalanced data, and provides$10.93\times$higher robustness against hardware noises than SOTA DNNs.