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Context: We apply the proposed NAS method with the supernet architecture described above. The depth of 5 stages is set to 3,4,7,4,11, respectively. The latency constraint is set to 2.5 ms that corresponds to the latency of EfficientNet-B1 on our target NPU, MIDAP. Table 6 compares our search results with the state-of-the-art models: EdgeTPU (Gupta and Akin, 2020), EfficientNet (Tan and Le, 2019a), Once-For-All (Cai et al., 2019). The latency of the other models is obtained by running the network on the MIDAP cycle-accurate simulator. We compare the accuracy without quantization, assuming that quantization effects will be similar to all models. As shown in Table 6, the baseline model, ours-M, found by the proposed NAS technique has higher accuracy than the other models on our target NPU; ours-M achieves more than 1.7% higher top-1 accuracy than EfficientNet-lite2 with similar latency. Moreover, it is 0.5% higher than EfficientNet-B1, even without using SE and h-swish activation function. Note that the number of parameters and the number of FLOPS in ours-M is larger than EfficientNet-B1. It implies that the complexity of the network is not a direct indicator of the end-to-end latency of the network. The end-to-end latency depends on the NPU architecture, and the proposed NAS technique could find a larger network with shorter latency by adding the latency factor to the loss function directly. The main benefit comes from different block assignment to stages. Finally, we selectively removed SE blocks from ours-XL+, resulting in ours-XL-rmSE+. We collected the activation values using randomly sampled 10K images from the training dataset and calculated the metric explained in Sec. 4.3.3.After removing SE blocks from ours-XL+ based on the metric, only about 60% of the blocks in the network have SE blocks. As a result, we could make the latency shorter, while the accuracy was slightly improved than ours-XL+. This model achieves 82.72% top-1 accuracy with only 11.66ms latency.It is much better than EfficientNet-EdgeTPU-L (Gupta and Akin, 2020) that achieves 80.62% FP32 top-1 accuracy with more than 20ms on EdgeTPU. Our architecture on MIDAP is about 2 times faster with 2.1% higher accuracy. In this work, we propose a fast NPU-aware NAS methodology extending the Single-Path NAS technique (Stamoulis et al., 2019). We modify the supernet architecture by varying the number of blocks in stages and adding mixed depthwise convolution (Tan and Le, 2019b) to the search space. By modifying the loss function to directly include the target latency estimated by a cycle-accurate simulator of the target NPU, we could find a better baseline architecture with a shorter latency than the latency constraint. Using a tight latency constraint, we can reduce the search space to find the baseline network fast. Afterward, we apply compound scaling to find a larger network than the baseline network, and add SE blocks and h-swish activation functions in the post-processing step.Through the proposed NAS methodology, we could obtain a network with 82.72% accuracy with 11.66ms latency on our target NPU, without special data augmentation in training. It dominates the existing network models on the target NPU. It confirms the importance of supernet architecture design for a given NPU and effectiveness of the three-step approach in the proposed NAS methodology: supernet design, SinglePath NAS with a tighter latency constraint, and compound scaling and post-processing. Experiments show that the proposed NAS technique could improve the accuracy-latency tradeoff over existing SoTA CNN models. Our best model achieves 82.72% top-1 accuracy on ImageNet with 11.66ms latency without any special data augmentation. Note that the latency is estimated by cycle-accurate simulation. For a fair comparison with the related work, the latency of each compared network is also estimated with the same simulator.

Question: How does the authors claim that the proposed method could improve the accuracy-latency tradeoff over existing SoTA CNN models?
They compared their network with state-of-the-art models in Table 6. Table 6 shows that the baseline model achieved higher accuracy than comparisons with similar latency.