用于TVM堆叠器堆栈的Transprecision张量加速器覆盖的敏捷自动调整
Agile Autotuning of a Transprecision Tensor Accelerator Overlay for TVM Compiler Stack
摘要：
专门用于张量运算的加速器，如分块矩阵运算和多维卷积，已经成为高性能深度学习计算的强大架构选择。框架、模型和精度选项的快速发展对这种张量加速器的适应性提出了挑战，因为适应新的要求会产生巨大的工程成本。可编程张量加速器提供了一种有前途的替代方案，它允许重新配置覆盖在物理FPGA可配置结构之上的虚拟架构。我们提出了一个覆盖（τ-VTA）和一个由敏捷启发的自动调整技术指导的优化方法。我们实现了比最先进技术更高的性能和更快的收敛速度。
英文摘要：
Specialized accelerators for tensor-operations, such as blocked-matrix operations and multi-dimensional convolutions, have been emerged as powerful architecture choices for high-performance Deep-Learning computing. The rapid development of frameworks, models, and precision options challenges the adaptability of such tensor-accelerators since the adaptation to new requirements incurs significant engineering costs. Programmable tensor accelerators offer a promising alternative by allowing reconfiguration of a virtual architecture that overlays on top of the physical FPGA configurable fabric. We propose an overlay (τ-VTA) and an optimization method guided by agile-inspired auto-tuning techniques. We achieve higher performance and faster convergence than state-of-art.
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。
