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Abstract Machine learning (ML) models are used for in-situ monitoring in additive manufacturing (AM) for defect detection. However, sensitive information stored in ML models, such as part designs, is at risk of data leakage due to unauthorized access. To address this, differential privacy (DP) introduces noise into ML, outperforming cryptography, which is slow, and data anonymization, which does not guarantee privacy. While DP enhances privacy, it reduces the precision of defect detection. This paper proposes combining DP with Hyperdimensional Computing (HDC), a brain-inspired model that memorizes training sample information in a large hyperspace, to optimize real-time monitoring in AM while protecting privacy. Adding DP noise to the HDC model protects sensitive information without compromising defect detection accuracy. Our studies demonstrate the effectiveness of this approach in monitoring anomalies, such as overhangs, using high-speed melt pool data analysis. With a privacy budget set at 1, our model achieved an F-score of 94.30%, surpassing traditional models like ResNet50, DenseNet201, EfficientNet B2, and AlexNet, which have performance up to 66%. Thus, the intersection of DP and HDC promises accurate defect detection and protection of sensitive information in AM. The proposed method can also be extended to other AM processes, such as fused filament fabrication. 

Introducing HyperSense, the 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, HyperSense analyzes real‐time raw low‐precision sensor data, offering advantages in handling noise, memory‐centricity, and real‐time learning. The 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 the solution's superior performance, evidenced by the highest area under the curve and sharpest receiver operating characteristic curve among lightweight models. Hardware‐wise, the field programmable gate array‐based domain‐specific accelerator tailored for HyperSense achieves a 5.6× 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.