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AutoDeepHLS: Deep Neural Network High-level Synthesis using fixed-point precision


Publication Type:

Conference/Workshop Paper


International Conference on Artificial Intelligence Circuits and Systems


Deep Neural Networks (DNN) have received much attention in various applications such as visual recognition, self-driving cars, health care, etc. Hardware implementation, specifically using FPGA and ASIC due to their high performance and low power consumption, is considered an efficient method. However, implementation on these platforms is difficult for neural network designers since they usually have limited knowledge of hardware. High-Level Synthesis (HLS) tools can act as a bridge between high-level DNN designs and hardware implementation. Nevertheless, these tools usually need implementation at the C level, whereas the design of neural networks is usually performed at a higher level (such as Keras or TensorFlow). In this paper, we propose a fully automated flow for creating a C-level implementation that is synthesizable with HLS Tools. Various aspects such as performance, minimal access to memory elements, data type knobs, and design verification are considered. Our results show that the generated C implementation is much more HLS friendly than previous works. Furthermore, a complete flow is proposed to determine different fixed-point precisions for network elements. We show that our method results in 25% and 34% reduction in bit-width for LeNet and VGG, respectively, without any accuracy loss.


author = {Mohammad Riazati and Masoud Daneshtalab and Mikael Sj{\"o}din and Bj{\"o}rn Lisper},
title = {AutoDeepHLS: Deep Neural Network High-level Synthesis using fixed-point precision},
booktitle = {International Conference on Artificial Intelligence Circuits and Systems},
url = {}