Bayesian Neural Networks (BNNs) offer robust uncertainty estimation capabilities through probabilistic modeling, yet their prohibitively high computational complexity and resource consumption limit deployment in edge computing. In this paper, we propose an FPGA-based BNN inference accelerator that optimizes critical mo...
Xiao-Tao Jia, Bing-Yue Zhang, Zigui Wu et al.· IEEE Transactions on Circuit...· 1 citation
Evaluations demonstrate that MegaGraph enables training on large-scale graphs where state-of-the-art baselines fail due to out-of-memory (OOM) errors, and achieves up to 4.51 times training speedup while maintaining model accuracy.
Tong Qiao, Ao Zhou, Ying-Jie Qi et al.· 0 citations
Vision-Language-Action (VLA) models have emerged as a promising foundation for Embodied AI, but their high inference cost poses significant challenges for deployment in robotic systems. In practice, on-device inference is constrained by limited compute capacity and energy budgets, struggling to simultaneously satisfy r...
Ao Zhou, Bo Dai, Le Yu et al.· Advanced Parallel Programmin...· 1 citation
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