You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: The existing hardware-aware differentiable NAS methods mostly define some hyperparameters to balance between accuracy and latency, including SinglePath NAS, whose loss function is defined as Eq. (6). Since there is no information on the target latency in the loss function, in case there is a strict latency constraint, they have to pay additional search costs for the hyperparameters to let the final architecture have no larger latency than the constraint. In addition, this process needs to be repeated whenever the target latency is changed. 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. Although this is not a panacea, this modification significantly eases the search process, which will be discussed in section 5.2 with various experiments.

Question: How the proposed loss function is different from that of original Single-Path NAS?
Previous method needs additional search cost for hyperparameter, since they have no information for target latency. The author's method directly includes the target latency, resulting in ease of search process.