Trustworthy Deep Learning Acceleration with Customizable Design Flow Automation
Zhiqiang Que, Hongxiang Fan, Gabriel Figueiredo, Ce Guo, Wayne W. Luk, Ryota Yasudo, Masato Motomura · 2025
In recent years, deep learning has brought the development of accurate and complex models across various domains. Deploying these models efficiently on resource-constrained platforms while maintaining high accuracy and trustworthiness, however, remains a critical challenge. This paper introduces an automated framework for optimizing trustworthy deep learning by enabling trade-off between three metrics: computational efficiency, trustworthiness, and predictive accuracy. Traditional compression techniques such as pruning and scaling reduce computational complexity but can compromise model calibration and uncertainty quantification, which is critical for safety-critical applications. To address this challenge, we integrate Monte Carlo Dropout (MCD) for Bayesian Convolutional Neural Networks (BayesCNNs) and propose an automated Design Space Exploration (DSE) approach driven by Bayesian Optimization to identify Pareto-optimal configurations. Our framework dynamically tunes pruning rates, dropout probabilities, and other parameters to achieve Pareto-optimal trade-offs between accuracy, efficiency, and uncertainty estimation. Two BayesCNN architectures are evaluated to demonstrate that our approach can systematically optimize deep learning models for trustworthiness and efficiency. Our results show that no single configuration is optimal across all metrics, demonstrating the need to automate and customize co-optimization strategies. Compared to state-of-the-art FPGA implementations, our optimized design achieves up to 7.67× faster inference and 12.8× higher energy efficiency while maintaining well-calibrated uncertainty estimates.