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Top1. Introduction
System on Chip (SoC) is a tiny but complete computer, which consists almost all components of a computer such as CPU, GPU and memory. Because of SoC’s advantage of low power consumption, these chips are wildly embedded into mobile systems. Recently mainstream SoCs include Atom from Intel, Tegra from Nvidia, Snapdragon from Qualcomm, Ax from Apple, MTx from MediaTek, etc, where x means number(s). SoC runs on multiple operation systems such as Android (Alejandro Acosta & Francisc Almeida, 2014; Alejandro Acosta & Francisco Almeida, 2014a), Linux and Windows.
Existing research topics of SoC include performance analysis (A. Acosta & F. Almeida, 2014; Alejandro Acosta & Francisco Almeida, 2014b; Papadopoulos et al., 2014), power consumption (Grasso, Radojkovic, Rajovic, Gelado, & Ramirez, 2014; Papadopoulos et al., 2014; Zhan, Lung, & Srivastava, 2014), etc. Similar with regular GPU in desktop or notebook, SoC embedded GPU (SoC GPU) is responsible for graphics processing for SoC (Giles & Reguly, 2014). Companies develop different architectures for SoC GPU for example Apple Ax’s PowerVR, Tegra K1’s Kepler (Singh & Jain, 2014), etc.
Tegra K1 is one of Nvidia’s latest SoCs which include 32-bit version and 64-bit version. Tegra K1 32-bit version is released in 2014, and Tegra K1 64-bit version is on developing. Tegra K1 32-bit is fabricated by 28nm HPM. Tegra is developed for applications such as rendering (Mobeen & Lin, 2012; Rodríguez & Alcocer, 2012; Q. Wang, Yu, Rasmussen, & Yu, 2014), ray tracing (Lee et al., 2013), optical flow (Plyer, Le Besnerais, & Champagnat, 2014), face recognition (Kwang-Ting & Yi-Chu, 2011; Y.-C. Wang, Donyanavard, & Cheng, 2012), object tracking (Růžička & Mašek, 2014), computational photography (Pulli & Troccoli, 2014) and sift detector (Rister, Guohui, Wu, & Cavallaro, 2013).
A wavelet is a mathematical function for decomposing a given function into different scale components, wavelet is applied to digital signal processing for decades. Wavelet transform generally includes two categories: continuous wavelet transform and discrete wavelet transform. Discrete Wavelet Transform (DWT) is a category of wavelet transform with discrete wavelet coefficients (Press, 2007), and there are two mainstream DWT implementation algorithms: filter bank convolution and the lifting scheme (Jung, Park, & Kim, 2005).