Towards Uncertainty-Quantifiable Biomedical Intelligence: Mixed-signal Compute-in-Entropy for Bayesian Neural Networks

To enhance AI robustness of mission-critical biomedical applications, Bayesian Neural Networks (BNNs) are instrumental for their structured approach to AI uncertainty estimation. However, implementing BNNs on edge devices is challenging due to significant resource demands for dynamic model updates and extensive inference sampling. Addressing this, we introduce a novel mixed-signal Compute-in-Memory with Entropy (CIE) hardware architecture that segregates dynamically-generated weights into analog entropy and digital parameters within a compute-in-memory framework, greatly reducing hardware overhead. We conducted thorough evaluations of the CIE architecture, assessing its performance against varying hardware imperfections, such as digital quantization errors, analog distribution imperfections, and device process variations, with a focus on both general and specialized tasks like Ventricular Arrhythmia (VA) detection. Our contributions include (1) a generic BNN acceleration strategy suitable for various CIM techniques and emerging devices, (2) a custom circuit design that improves hardware efficiency by 19.2×-440× compared to existing BNN accelerators, (3) a CIE-based BNN for VA detection enhancing accuracy, reducing uncertainty estimation time and energy/latency to 1.29μJ/1.55ms, and (4) identification of tolerable quantization error and device variation limits for BNNs in uncertainty estimation.