TINA: Acceleration of Non-NN Signal Processing Algorithms Using NN Accelerators

Christiaan Boerkamp, Steven van der Vlugt, Zaid Al-Ars
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Abstract

This paper introduces TINA, a novel framework for implementing non Neural Network (NN) signal processing algorithms on NN accelerators such as GPUs, TPUs or FPGAs. The key to this approach is the concept of mapping mathematical and logic functions as a series of convolutional and fully connected layers. By mapping functions into such a small substack of NN layers, it becomes possible to execute non-NN algorithms on NN hardware (HW) accelerators efficiently, as well as to ensure the portability of TINA implementations to any platform that supports such NN accelerators. Results show that TINA is highly competitive compared to alternative frameworks, specifically for complex functions with iterations. For a Polyphase Filter Bank use case TINA shows GPU speedups of up to 80x vs a CPU baseline with NumPy compared to 8x speedup achieved by alternative frameworks. The framework is open source and publicly available at https://github.com/ChristiaanBoe/TINA.
TINA:使用网络加速器加速非网络信号处理算法
本文介绍了 TINA,这是一种在 GPU、TPUs 或 FPGA 等神经网络加速器上实现非神经网络(NN)信号处理算法的新型框架。这种方法的关键在于将数学和逻辑函数映射为一系列卷积层和全连接层的概念。通过将函数映射到如此小的 NN 层子包中,就有可能在 NN 硬件(HW)加速器上高效执行非 NN 算法,并确保 TINA 实现可移植到任何支持此类 NN 加速器的平台上。结果表明,与其他框架相比,TINA 具有很强的竞争力,特别是在复杂函数迭代方面。在多相滤波器库使用案例中,TINA 的 GPU 速度是使用 NumPy 的 CPU 基线速度的 80 倍,而其他框架的速度仅为 8 倍。该框架是开源的,可在https://github.com/ChristiaanBoe/TINA。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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