Presentation
Towards Uncertainty-aware Robotic Perception via Mixed-signal BNN Engine Leveraging Probabilistic Quantum Tunneling
DescriptionIntegrating deep learning with environmental perception enhances robotic adaptability to complex tasks. However, its ``black-box'' nature, such as the lack of uncertainty quantification, poses challenges for safety-critical applications, particularly in unstructured and noisy environments. Bayesian neural networks (BNNs) offer uncertainty quantification but are limited by high hardware overhead, restricting real-time implementation on resource-constrained robots. This paper presents a mixed-signal hardware accelerator for BNNs, utilizing probabilistic quantum tunneling in fully depleted silicon-on-insulator (FD-SOI) transistors to enable efficient, real-time uncertainty quantification. Device measurements indicate high-quality Gaussian random variable generation, validated through quantile-quantile plot analysis, with a high correlation coefficient (r = 0.997) at 200 fJ/sample. Leveraging such compact randomness, the parallel architecture achieved 10^3--10^4× latency reduction at less than 2 × area cost. Finally, in uncertainty-aware visual localization application of autonomous underwater vehicles, the BNN model effectively distinguishes data noise from model uncertainty, yielding significant information gain and enhancing the resampling efficiency by 4.5× at same accuracy.
Event Type
Research Manuscript
TimeTuesday, June 244:45pm - 5:00pm PDT
Location3003, Level 3
Design
DES4: Digital and Analog Circuits