We present a 65-nm risk-aware multimodal Bayesian inference engine for privacy-preserving, fully on-device skin lesion screening under uncontrolled at-home conditions. The proposed compute-in-memory architecture performs in-word Mixture-of-Gaussian sampling, improving uncertainty modeling beyond conventional unimodal Bayesian neural networks. This added probabilistic expressiveness increases equal-risk operating coverage by 1.4x, improves robustness to user-data perturbations by >1.5x, enhances process-variation resilience by 5.5x, and improves balanced accuracy by 1.8% over state-of-the-art unimodal Bayesian neural networks. Hardware robustness is further supported by calibration-free Gaussian random-number generation using complementary process variation, achieving 16.3 fJ/sample and 168.6 GSa/s/mm^2 efficiency. These results demonstrate a practical, energy-efficient, and risk-aware edge-AI solution for privacy-conscious medical screening.

翻译：本文提出一种65纳米风险感知多模态贝叶斯推理引擎，用于在不受控居家条件下实现隐私保护的完全端侧皮肤病变筛查。所提出的存内计算架构执行词内高斯混合采样，相比传统单模态贝叶斯神经网络提升了不确定性建模能力。这种概率表达能力的增强使等风险操作覆盖率提升1.4倍，对用户数据扰动的鲁棒性提高1.5倍以上，工艺变化适应性增强5.5倍，平衡准确率较最先进单模态贝叶斯神经网络提升1.8%。硬件鲁棒性通过利用互补工艺变化的免校准高斯随机数生成进一步增强，实现了每样本16.3飞焦和168.6吉采样/秒/平方毫米的效率。这些结果证明了该方案作为面向隐私敏感医疗筛查的实用、高能效且风险感知的边缘人工智能解决方案的可行性。