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：智能助手、可穿戴健康监测设备和情境感知系统对隐私保护的个性化数据分析需求日益增长，亟需兼具高能效与安全性的硬件。本文提出一种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超维计算的物理不可克隆熵源。所提出的2T-2T熵元胞实现了紧凑型、设备专属且免写入的条目存储器，可在无需存储传统存储器中随机基向量的情况下完成隐私保护的生物信号编码。原型芯片实测单次编码能耗为7.13纳焦，条目存储密度为2.38兆比特/平方毫米，单次预测耗能76.44纳焦，单次训练更新耗能357.32纳焦。该芯片支持原位决策、持续学习及面向多用户部署和冷启动个性化的联邦学习。生物信号数据集评估结果显示，在肌电图数据集上准确率达93.2%，在UCI-HAR数据集上达96.1%，同时相较二值超维计算将超向量维度降低14.3倍。这些成果证明，该平台可为实现安全边缘生物医学智能提供兼具高能效与隐私保护能力的神经形态硬件方案。