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Keyword: hardware-aware design ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

   Wu, Keutzer, Dai et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2019

   The paper presents a gradient-based architecture search that designs mobile-friendly ConvNets (FBNets) with accuracy and speed it reports as better than manual and automated designs, at a low search cost.

   Unchecked3 claims
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   1. UncheckedFBNet-B reaches 74.1% ImageNet top-1 accuracy at 295M FLOPs and 23.1 ms on a Samsung S8, 2.4x smaller and 1.5x faster than MobileNetV2-1.3.“FBNet-B achieves 74.1% top-1 accuracy on ImageNet with 295M FLOPs and 23.1 ms latency on a Samsung S8 phone, 2.4x smaller and 1.5x faster than MobileNetV2-1.3 with similar accuracy.”
   2. UncheckedThe authors estimate FBNet-B's architecture search cost is 420 times smaller than MnasNet's, at 216 GPU-hours, with higher accuracy and lower latency.“Despite higher accuracy and lower latency than MnasNet, we estimate FBNet-B's search cost is 420x smaller than MnasNet's, at only 216 GPU-hours.”
   3. UncheckedWhen searched for different input resolutions and channel sizes, FBNets reach 1.5% to 6.4% higher accuracy than MobileNetV2.“Searched for different resolutions and channel sizes, FBNets achieve 1.5% to 6.4% higher accuracy than MobileNetV2.”