# DeepReShape: Redesigning Neural Networks for Efficient Private Inference Source: [DeepReShape: Redesigning Neural Networks for Efficient Private Inference]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:35:02.733Z Authors: Nandan Kumar Jha, Brandon Reagen Published: 2023-04-20T18:27:02.000Z ## Abstract Prior work on Private Inference (PI) -- inferences performed directly on encrypted input -- has focused on minimizing a network's ReLUs, which have been assumed to dominate PI latency rather than FLOPs. Recent work has shown that FLOPs for PI can no longer be ignored and incur high latency penalties. In this paper, we develop DeepReShape, a technique that optimizes neural network architectures under PI's constraints, optimizing for both ReLUs and FLOPs for the first time. The key insight is strategically allocating channels to position the network's ReLUs in order of their criticality to network accuracy, simultaneously optimizes ReLU and FLOPs efficiency. DeepReShape automates network development with an efficient process, and we call generated networks HybReNets. We evaluate DeepReShape using standard PI benchmarks and demonstrate a 2.1% accuracy gain with a 5.2$\\times$ runtime improvement at iso-ReLU on CIFAR-100 and an 8.7$\\times$ runtime improvement at iso-accuracy on TinyImageNet. Furthermore, we investigate the significance of network selection in prior ReLU optimizations and shed light on the key network attributes for superior PI performance. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on CIFAR-100 (Acc. (%) and Acc./#ReLU)](): Image Classification; CIFAR-100; Accuracy (%): 77.14. ## Original sources and implementation [Original paper]() [PDF]()