most citedEnhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA

1 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2025★ 1 cited

FPGA-based Acceleration for Convolutional Neural Networks: A Comprehensive Review

Junye Jiang, Yaan Zhou, Yuanhao Gong +2

Convolutional Neural Networks (CNNs) are fundamental to deep learning, driving applications across various domains. However, their growing complexity has significantly increased co…

cs.LG2024★ 1 cited

Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA

Hao Mark Chen, Liam Castelli, Martin Ferianc +4

Reliable uncertainty estimation plays a crucial role in various safety-critical applications such as medical diagnosis and autonomous driving. In recent years, Bayesian neural netw…