优化端到端CNN加速的全生命周期成本
Optimizing the Whole-life Cost in End-to-end CNN Acceleration
摘要：
自CNN在计算机视觉领域取得成功以来，其加速问题受到越来越多的关注。由于卷积层的加速器无法处理异构的功能层，现代端到端CNN加速解决方案要么将各种计算转换为矩阵/向量运算，从而失去卷积中的数据重用机会，要么为每种层引入专用功能单元，从而导致利用不足和高更新费用。为了提高整个生命周期的成本效率，我们需要一种加速解决方案，该解决方案在处理CNN层时是有效的，并且具有适用于各种现有和新兴层的通用性。为此，我们提出了GCONV Chain，一种将整个CNN计算转换为标准通用卷积（GCONV）链的方法，该方法可以由现有的CNN加速器有效地处理。本文全面分析了GCONV Chain模型，并提出了一种支持GCONV Chain的全栈实现。一方面，七种CNN的结果表明，GCONV Chain将现有CNN加速器的性能和能效平均提高了3.4倍和3.2倍。另一方面，我们表明GCONV Chain为CNN加速提供了较低的整个生命周期成本，包括开发人员的努力和用户的总拥有成本。
英文摘要：
The acceleration of CNNs has gained increasing atten-tion since their success in computer vision. With the heterogeneous functional layers that cannot be pro-cessed by the accelerators proposed for convolution layers only, modern end-to-end CNN acceleration so-lutions either transform the diverse computation into matrix/vector arithmetic, which loses data reuse op-portunities in convolution, or introduce dedicated functional unit to each kind of layer, which results in underutilization and high update expense. To enhance the whole-life cost efficiency, we need an acceleration solution that is efficient in processing CNN layers and has the generality to apply to all kinds of existing and emerging layers. To this end, we pro-pose GCONV Chain, a method to convert the entire CNN computation into a chain of standard general convolutions (GCONV) that can be efficiently pro-cessed by the existing CNN accelerators. This paper comprehensively analyzes the GCONV Chain model and proposes a full-stack implementation to support GCONV Chain. On one hand, the results on seven var-ious CNNs demonstrate that GCONV Chain improves the performance and energy efficiency of existing CNN accelerators by an average of 3.4x and 3.2x re-spectively. On the other hand, we show that GCONV Chain provides low whole-life costs for CNN accelera-tion, including both developer efforts and total cost of ownership for the users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
