Modern CPU’s memory data access speed is much lower than the calculation speed. Data visits are often a bottleneck of program performance. Cache has a higher data access speed compared to memory, which can load data in memory in advance to improve the efficiency of CPU access to memory data. The cache is usually a multi-level structure. The closer to the CPU level, the faster the cache visits. The cache miss is that the data which CPU needs to be visited is not in the current cache. The processor needs to read data from the next level of cache or memory. The occurrence of cache misses will reduce the memory performance of the CPU. Prefetch can load the data visited by the processor to the cache in advance, reduce the occurrence of a cache miss, improve the efficiency of access to access, and then improve program performance. Related scholars have studied key technologies in different scenarios and on key technologies such as prefetch data, prefetch distance, and prefetch methods. However, related studies are mainly aimed at specific scenarios or access modes, and less prefetch research on general programs. For the prefetch optimization problem of general programs, this article proposes value evaluation model and automatic implementation method of software prefetching in general program optimization. The model can automatically analyze the data that needs to be prefetch and the method can automatically insert the data prefetch into the program. Based on the value evaluation model and automatic implementation method, this article implements the automatic software prefetch tool SP-Frame under the LLVM compiler. In the Crono Benchmark, the SP-Frame tool can reduce 54 %of the L1 cache miss on average, and the average speedup has achieved 23%.
This paper presents a deep learning-based framework for automatic license plate detection and recognition in nature scene images. To start with, a small model is developed for license plate detection, based on cascaded convolutional neural network (CNN). The CNN cascade works on multiple levels. The early levels quickly scan low-resolution candidate windows and reject most of the nonplate regions, and the late levels carefully evaluate a small number of candidate windows in high-resolution. The detected candidate regions are cropped from the original image for recognition. Next, we treat plate recognition as a sequence labeling problem and use a combination of CNN and recurrent neural network for feature extraction and learning. The output result is then decoded to a readable character sequence using a connectionist temporal classification layer. This plate recognition model is segment-free and can be trained end-to-end. Finally, the generative adversarial network is employed to automatically generate image samples for training the plate recognition model. Experimental results on extensive datasets prove the effectiveness and efficiency of the proposed framework.
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