Proposes dropout-based BayesCVNNs with automated configuration search and FPGA accelerators that deliver 4.5x–13x speedups over GPUs while enabling uncertainty estimation for complex-valued neural networks.
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Algorithm and Hardware Co-Design for Efficient Complex-Valued Uncertainty Estimation
Proposes dropout-based BayesCVNNs with automated configuration search and FPGA accelerators that deliver 4.5x–13x speedups over GPUs while enabling uncertainty estimation for complex-valued neural networks.