The increasing demand for privacy-preserving personal data analytics in smart assistants, wearable health monitors, and context-aware systems calls for hardware that is both energy-efficient and secure. This work presents a 65-nm privacy-preserving neuromorphic encoder that leverages transistor-level process variation as physically unclonable entropy for hyperdimensional computing. The proposed 2T-2T entropy cell enables compact, device-specific, and write-free item memory, allowing privacy-preserving bio-signal encoding without storing random basis vectors in conventional memory. The fabricated prototype achieves 7.13 nJ per encoding, 2.38 Mb/mm^2 item-memory density, 76.44 nJ per prediction, and 357.32 nJ per training update. It also supports in-situ decision-making, continual learning, and federated learning for multi-user deployment and cold-start personalization. Evaluations across bio-signal datasets demonstrate 93.2% accuracy on EMG and 96.1% accuracy on UCI-HAR, while reducing hypervector dimensionality by 14.3x compared with binary hyperdimensional computing. These results demonstrate an energy-efficient and privacy-preserving neuromorphic hardware platform for secure edge biomedical intelligence.

翻译：智慧助手、可穿戴健康监测器和情境感知系统对隐私保护个人数据分析的需求日益增长，这要求硬件兼具高能效与安全性。本工作提出一种65纳米隐私保护神经形态编码器，利用晶体管级工艺变异作为物理不可克隆熵源用于超维计算。所提出的2T-2T熵池单元实现了紧凑型、设备特定且免写入的项目存储器，无需在传统存储器中存储随机基向量即可实现隐私保护的生物信号编码。制作的原型实现了每次编码7.13纳焦、项目存储器密度2.38兆比特/平方毫米、每次预测76.44纳焦及每次训练更新357.32纳焦的性能。该编码器还支持原位决策、持续学习以及面向多用户部署和冷启动个性化的联邦学习。在生物信号数据集上的评估表明，在肌电信号上准确率达93.2%，在UCI-HAR数据集上准确率达96.1%，同时与二进制超维计算相比，超向量维度降低了14.3倍。这些结果展示了一种用于安全边缘生物医学智能的高能效且隐私保护的神经形态硬件平台。