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Context: Figure 6 depicts our building block structure. This block starts and ends with 1×1 convolution, with N searchable superkernels in the middle. Each searchable superkernel is designed similarly to Eq. (3), while we may use different threshold values in each superkernel. The kernel sizes and expansion ratios are selected among predetermined values. If the j-th searchable superkernel chooses an expansion ratio e_{j}, the j-th kernel has e_{j} times more channels than the first 1×1 convolution. Compared with the original MixConv suggested in (Tan and Le, 2019b), the proposed building block supports more diverse combinations of kernel sizes and expansion ratios. It enhances the efficiency of search results on our target NPU (Table 5). A superkernel has two parameters to search: expansion ratio and kernel size. To limit the search space, we choose the expansion ratio among 0, 2, 4, and 6, and the kernel size between 3 and 5 when MBConv or full convolution is used as the building block. In the case of the MixConv-based building block, we use N=3 superkenels whose expansion ratio is 0 or 2; The sum of the expansion ratio of three superkernels has the same range as the expansion ratio of a single MBConv block. To allow three superkernels to have different kernel sizes, we let one of three superkernels be able to have 7 as the kernel size. A NAS technique explores a predefined search space and estimates the performance for each candidate architecture to find an optimal one with the highest accuracy under a given latency constraint. Thus there are three factors that affect the performance of NAS, as shown in Figure 1: search space, search strategy, and performance estimation. The search space of a NAS technique is usually restricted by a supernet that defines the topology of the largest network to explore. Since the performance of a network depends on the hardware platform, the NAS technique needs to be customized to a given hardware platform. While numerous NAS techniques have been proposed with various search strategies recently, their assumed hardware platforms are mostly GPUs. In this paper, we present a customized NAS technique for an NPU, which produces a CNN architecture with a better accuracy-latency tradeoff than existing models.

Question: How "superkernel" is different from supernet?
A superkernel is a component for searching expansion ratio and kernel sizes. Supernet defines the largest network we can search.