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

TL;DR

TINA is a framework that enables non-NN signal processing algorithms to run efficiently on NN accelerators by mapping functions into NN layers, achieving significant speedups and broad hardware compatibility.

Contribution

It introduces a novel method to implement non-NN algorithms on NN hardware by representing functions as NN layers, enhancing portability and performance.

Findings

Up to 80x GPU speedup for Polyphase Filter Bank on GPU

TINA outperforms alternative frameworks for complex iterative functions

Open source implementation available at GitHub

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.