TinyHLS, a Python-based Hardware Compiler for 1D and 2D Convolutional Neural Networks

Abstract

TinyHLS is a Python-based hardware compiler that automatically generates hardware accelerators in the form of hardware description language (HDL) code for Convolutional Neural Networks (CNNs). The description of the CNN architecture as well as the training is done in advance using Python TensorFlow Keras. TinyHLS reduces the development effort to implement inference calculations in digital hardware regarding cost and time. Furthermore, tinyHLS offers a platform independent alternative to commercial high-level synthesis tools like AMD Vivado HLS TM [ 1 ]. Each hardware accelerator generated by tinyHLS is a full hardware implementation of its CNN, allowing low-latency and low-power inference. In this work, the concept of this hardware compiler is presented. The workflow of tinyHLS is demonstrated based on a smart farming use case. For this use case a CNN to detect oranges in images is developed in TensorFlow Keras, translated using tinyHLS and implemented on a field programmable gate array (FPGA). The results in terms of accuracy, latency, energy consumption and hardware requirements are then discussed based on the implementation of the use case 144 CNN. Finally, a brief outlook on the improvement of tinyHLS is given to meet requirements of edge artificial intelligence (AI) computing in the future.