论文标题
硬件感知的移动构建块评估计算机视觉
Hardware-aware mobile building block evaluation for computer vision
论文作者
论文摘要
在这项工作中，我们提出了一种方法，以准确评估和比较有效的神经网络构建块的性能，以硬件感知方式进行计算机视觉。我们的比较使用了基于设计空间的随机采样网络的帕累托前沿来捕获潜在的准确性/复杂性权衡。我们表明，我们的方法允许通过以前的比较范例获得的信息匹配，但对硬件成本和准确性之间的关系提供了更多见解。我们使用我们的方法来分析不同的构建块，并在一系列嵌入式硬件平台上评估它们的性能。这凸显了基准构建块作为神经网络设计过程中的预选步骤的重要性。我们表明，选择正确的构件可以在特定的硬件ML加速器上加快推理的速度2倍。
In this work we propose a methodology to accurately evaluate and compare the performance of efficient neural network building blocks for computer vision in a hardware-aware manner. Our comparison uses pareto fronts based on randomly sampled networks from a design space to capture the underlying accuracy/complexity trade-offs. We show that our approach allows to match the information obtained by previous comparison paradigms, but provides more insights in the relationship between hardware cost and accuracy. We use our methodology to analyze different building blocks and evaluate their performance on a range of embedded hardware platforms. This highlights the importance of benchmarking building blocks as a preselection step in the design process of a neural network. We show that choosing the right building block can speed up inference by up to a factor of 2x on specific hardware ML accelerators.
