信号处理与深度学习硬件加速的一致性计算结构

在计算需求层面对多种典型信号处理算法与深度学习算法进行了分析与模块化分解，提取了两类应用共有的且适合并行硬件加速的计算模块，提出了信号处理与深度学习的一致性计算模型，并基于一致性计算模型设计了控制与计算分离的层次化处理单元与阵列化计算结构。通过对不同应用计算过程的软件定义能够实现信号处理与深度学习的一致性硬件加速计算，基于Zynq计算平台从重构效率与计算性能两个方面对一致性计算模型与计算结构进行了验证，结果表明：基于一致性计算模型的软件定义可重构计算结构，具有较高的计算性能与重构效率。

Hardware-accelerated consistent computing structure for signal processing and deep learning

A variety of typical signal processing algorithms and deep learning algorithms were analyzed and modularized from the calculation requirements level. The computing modules, which were suitable for hardware acceleration parallelly in the two types of applications were extracted. A consistent computing model for signal processing and deep learning was proposed, and a hierarchical processing element and arrayed processing structure were proposed based on the consistent computing model in which the control part and computation part were separated. By the software definition of different application computing processes, the consistent hardware-accelerated computation of signal processing and deep learning could be realized flexibly.Based on Zynq computing platform, the consistency computing model and computing structure were verified from two aspects of reconstruction efficiency and computing performance. The validation results indicate that software-defined reconfigurable computing structures based on consistency computing models have high computational performance and reconstruction efficiency.