7 papers
Probabilistic Memory for Trustworthy Edge Intelligence
Likai Pei, Jiahao Zheng, Xueji Zhao +9
Probabilistic computation plays an important role in trustworthy edge intelligence to quantify uncertainty, enhance robustness, reconstruct data, and protect privacy, but its adopt…
A 65 nm Multi-Modal Bayesian Inference Engine with 16.3 fJ/Sample Calibration-Free GRNG for Risk-Aware At-Home Skin Lesion Screening
Steven Davis, Likai Pei, Jianbo Liu +5
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…
A 65-nm Privacy-Preserving Neuromorphic Encoder With 7.13-nJ Efficiency, 2.38-Mb/mm^2 Item-Memory Density, and Federated Learning Support
Boyang Cheng, Jianbo Liu, Steven Davis +5
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…
A 65 nm Trustworthy Hypoglycemia Forecasting Engine Achieving 11.3 nJ per Inference
Boyang Cheng, Jianbo Liu, Pengyu Ren +6
Diabetes affects millions of people and requires reliable continuous glucose monitoring for early hypoglycemia warning. However, medical AI systems must be not only accurate and en…
A Unified Memory Perspective for Probabilistic Trustworthy AI
Xueji Zhao, Likai Pei, Jianbo Liu +2
Trustworthy artificial intelligence increasingly relies on probabilistic computation to achieve robustness, interpretability, security and privacy. In practical systems, such workl…
A 65 nm Bayesian Neural Network Accelerator with 360 fJ/Sample In-Word GRNG for AI Uncertainty Estimation
Zephan M. Enciso, Boyang Cheng, Likai Pei +4
Uncertainty estimation is an indispensable capability for AI-enabled, safety-critical applications, e.g. autonomous vehicles or medical diagnosis. Bayesian neural networks (BNNs) u…