Hyperdimensional computing (HDC) enables efficient data encoding and processing in high-dimensional space, benefiting machine learning and data analysis. However, underutilization of these spaces can lead to overfitting and reduced model reliability, especially in data-limited systems a critical issue in sectors like healthcare that demand robustness and consistent performance. We introduce BoostHD, an approach that applies boosting algorithms to partition the hyperdimensional space into subspaces, creating an ensemble of weak learners. By integrating boosting with HDC, BoostHD enhances performance and reliability beyond existing HDC methods. Our analysis highlights the importance of efficient utilization of hyperdimensional spaces for improved model performance. Experiments on healthcare datasets show that BoostHD outperforms state-of-the-art methods. On the WESAD dataset, it achieved an accuracy of 98.37%, surpassing Random Forest, XGBoost, and OnlineHD. BoostHD also demonstrated superior inference efficiency and stability, maintaining high accuracy under data imbalance and noise. In person-specific evaluations, it achieved an average accuracy of 96.19%, outperforming other models. By addressing the limitations of both boosting and HDC, BoostHD expands the applicability of HDC in critical domains where reliability and precision are paramount.

In-situ sensing, in conjunction with learning models, presents a unique opportunity to address persistent defect issues in Additive Manufacturing (AM) processes. However, this integration introduces significant data privacy concerns, such as data leakage, sensor data compromise, and model inversion attacks, revealing critical details about part design, material composition, and machine parameters. Differential Privacy (DP) models, which inject noise into data under mathematical guarantees, offer a nuanced balance between data utility and privacy by obscuring traces of sensing data. However, the introduction of noise into learning models, often functioning as black boxes, complicates the prediction of how specific noise levels impact model accuracy. This study introduces the Differential Privacy-HyperDimensional computing (DP-HD) framework, leveraging the explainability of the vector symbolic paradigm to predict the noise impact on the accuracy of in-situ monitoring, safeguarding sensitive data while maintaining operational efficiency. Experimental results on real-world high-speed melt pool data of AM for detecting overhang anomalies demonstrate that DP-HD achieves superior operational efficiency, prediction accuracy, and robust privacy protection, outperforming state-of-the-art Machine Learning (ML) models. For example, when implementing the same level of privacy protection (with a privacy budget set at 1), our model achieved an accuracy of 94.43%, surpassing the performance of traditional models such as ResNet50 (52.30%), GoogLeNet (23.85%), AlexNet (55.78%), DenseNet201 (69.13%), and EfficientNet B2 (40.81%). Notably, DP-HD maintains high performance under substantial noise additions designed to enhance privacy, unlike current models that suffer significant accuracy declines under high privacy constraints.

Introducing HyperSense, our co-designed hardware and software system efficiently controls Analog-to-Digital Converter (ADC) modules' data generation rate based on object presence predictions in sensor data. Addressing challenges posed by escalating sensor quantities and data rates, HyperSense reduces redundant digital data using energy-efficient low-precision ADC, diminishing machine learning system costs. Leveraging neurally-inspired HyperDimensional Computing (HDC), HyperSense analyzes real-time raw low-precision sensor data, offering advantages in handling noise, memory-centricity, and real-time learning. Our proposed HyperSense model combines high-performance software for object detection with real-time hardware prediction, introducing the novel concept of Intelligent Sensor Control. Comprehensive software and hardware evaluations demonstrate our solution's superior performance, evidenced by the highest Area Under the Curve (AUC) and sharpest Receiver Operating Characteristic (ROC) curve among lightweight models. Hardware-wise, our FPGA-based domain-specific accelerator tailored for HyperSense achieves a 5.6x speedup compared to YOLOv4 on NVIDIA Jetson Orin while showing up to 92.1% energy saving compared to the conventional system. These results underscore HyperSense's effectiveness and efficiency, positioning it as a promising solution for intelligent sensing and real-time data processing across diverse applications.