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Deep Convolutional Neural Network (CNN) based methods have shown outstanding performance in a wide range of applications. Nowadays neural networks become deeper, leading to demand of substantial computation and memory resources. Customized hardware is one option which maintains high performance in lower energy consume than general CPUs or GPUs. While hardware designing, we need to address the problem...
For the sake of capacity and cost, disks are currently considered as the main storage medium for the massive data. However, the I/O bandwidth of disks lags far behind the growing speed of data, which thus becomes the performance bottleneck of the big data management systems. Therefore, optimizing the storage structure to improve the efficiency of reading and writing has become one important challenge...
HEVC doubles the coding efficiency with more than 4x coding complexity as compared to H.264/AVC. To alleviate the burden of Intra encoder, we estimate the RD-cost from the source image textures, and dynamically select two promising CU/PU mode candidates to execute exhaustive RDO processing. As integrated in our hardwired encoder, the averaged 61.7% computation complexity was saved with 4.53% rate...
Great amount of two-dimensional (2D) discrete cosine transforms and Hadamard transforms are executed in HEVC. Upon the end of real-time UHDTV Codec, the full pipeline variable block size 2D transform engine with the efficient hardware utilization is proposed to handle the DCT/IDCT and Hadamard transforms. The efficiency comes from two aspects. First, the hardware for small-size transforms is fully...
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