Jan 23, 2023 · The large number of ReLU non-linearity operations in existing deep neural networks makes them ill-suited for latency-efficient private inference ...
Feb 1, 2023 · We present an automated linearization method to train a DNN with limited ReLU budget for inference in yielding models able to perform significantly better than ...
Jan 23, 2023 · The large number of ReLU non-linearity operations in existing deep neural networks makes them ill-suited for latency-efficient private ...
The large number of ReLU non-linearity operations in existing deep neural net- works makes them ill-suited for latency-efficient private inference (PI).
The large number of ReLU non-linearity operations in existing deep neural networks makes them ill-suited for latency-efficient private inference (PI).
Private inference (PI) enables inference directly on cryptographically secure data. While promis- ing to address many privacy issues, it has seen.
Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference, Souvik Kundu, Shunlin Lu, Yuke Zhang, Jacqueline Tiffany Liu, Peter A ...
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Feb 8, 2024 · Learning to linearize deep neural networks for secure and efficient private inference. ICLR, 2023a. Souvik Kundu, Sairam Sundaresan, Massoud ...
Large number of ReLU and MAC operations of Deep neural networks make them ill-suited for latency and compute-efficient private inference. Model Optimization.
Aug 18, 2023 · In this paper, we propose PAPI, a Practical and Adaptive Private Inference framework. First, we develop an accuracy-adaptive neural architecture search (NAS) ...
Missing: Learning Linearize