Performance Comparison of CGRA and Mobile GPU for Light-field Image Processing
Performance Comparison of CGRA and Mobile GPU for Light-field Image Processing
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CGRA 与移动 GPU 光场图像处理的性能比较
DOI：
10.1109/candar.2016.0040
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Shinya TAKAMAEDA-YAMAZAKI and Yasuhiko NAKASHIMA
中科院分区：
文献类型：
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作者：
Yuttakon YUTTAKONKIT;Shinya TAKAMAEDA-YAMAZAKI and Yasuhiko NAKASHIMA
最近，许多方法将光场图像处理应用于智能手机和可穿戴设备。图形处理单元（GPU）通常用于在此类图像处理中利用并行性。然而，因为光场应用中的访问模式比典型的模板应用更稀疏，并且不使用缓存行中的所有数据。此外，对多个位置的数据请求会在缓存系统中产生巨大的短突发内存传输，导致高延迟，并且不能充分利用 GPU 的高内存带宽。因此，一种利用长突发数据传输来提高内存带宽利用率的替代架构至关重要。我们提出了一种面向稀疏模板的粗粒可重构加速器（CGRA），我们称之为 EMAXV。与 GPU 上按需加载多个数据不同，EMAXV 在执行之前通过长突发传输加载输入数据，以隐藏稀疏内存访问和多线程缓存竞争。它进一步掩盖了内存加载延迟与不同激活的执行延迟。我们使用相同的主机 CPU 频率和主内存带宽评估了 EMAXV 和移动 GPU (Tegra K1) 的性能。尽管EMAXV的计算能力要低得多，但我们在光场深度提取方面实现了移动GPU的四倍性能，在光场图像渲染方面达到了89%的性能。
Recently, many approaches apply light-field image processing on smartphones and wearable devices. A Graphic Processing Unit (GPU) is commonly used to exploit parallelism in such image processing. However, because the access pattern in the light-field application is more sparse than typical stencil applications and does not use all data in a cache line. Furthermore, the data requests to multiple locations generate enormous short-burst memory transfers in the cache system, cost high latency, and do not fully utilize the high memory bandwidth of GPU. Therefore, an alternative architecture that exploits a long-burst data transmission, which improves the memory bandwidth utilization, is essential. We propose a sparse stencil oriented Coarse Grain Reconfigurable Accelerator (CGRA) that we call EMAXV. Unlike on-demand multiple data loading on GPU, EMAXV loads the input data with a long burst transferring before the execution proceeds to conceal the sparse memory access and multi-threading cache races. It further obscures the memory loading latency with an execution latency from different activations. We evaluated the EMAXV and mobile GPU (Tegra K1) performances with identical host CPU's frequency and main memory bandwidth. Although EMAXV has much lower computation capability, we achieved four times performance of mobile GPU for light-field depth extraction and 89% of the performance for light-field image rendering.