Edge AI accelerators have been emerging as a solution for near customers' applications in areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable devices, robotics, and remote sensing satellites. These applications not only require meeting performance targets but also meeting strict area and power constraints due to their portable mobility feature and limited power sources. As a result, a column streaming-based convolution engine has been proposed in this paper that includes column sets of processing elements design for flexibility in terms of the applicability for different CNN algorithms in edge AI accelerators. Comparing to a commercialized CNN accelerator, the key results reveal that the column streaming-based convolution engine requires similar execution cycles for processing a 227 x 227 feature map with avoiding zero-padding penalties.
翻译:在无人驾驶飞行器、图像识别传感器、可磨损装置、机器人和遥感卫星等领域,AI加速器已成为接近客户应用的解决方案,这些应用不仅需要达到性能目标,而且由于其便携式机动性特点和有限的动力源,需要满足严格的面积和动力限制,因此,本文件提出了一个基于柱子流动的变速引擎,其中包括一系列处理要素设计,以灵活地适用于边缘AI加速器中不同的CNN算法。