A Methodology and Open-Source Tools to Implement Convolutional Neural Networks Quantized with TensorFlow Lite on FPGAs

Abstract

Convolutional neural networks (CNNs) are used for classification, as they can extract complex features from input data. The training and inference of these networks typically require platforms with CPUs and GPUs. To execute the forward propagation of neural networks in low-power devices with limited resources, TensorFlow introduced TFLite. This library enables the inference process on microcontrollers by quantizing the network parameters and utilizing integer arithmetic. A limitation of TFLite is that it does not support CNNs to perform inference on FPGAs, a critical need for embedded applications that require parallelism. Here, we present a methodology and open-source tools for implementing CNNs quantized with TFLite on FPGAs. We developed a customizable accelerator for AXI-Lite-based systems on chips (SoCs), and we tested it on a Digilent Zybo-Z7 board featuring the XC7Z020 FPGA and an ARM processor at 667 MHz. Moreover, we evaluated this approach by employing CNNs trained to identify handwritten characters using the MNIST dataset and facial expressions with the JAFFE database. We validated the accelerator results with TFLite running on a laptop with an AMD 16-thread CPU running at 4.2 GHz and 16 GB RAM. The accelerator’s power consumption was 11× lower than the laptop while keeping a reasonable execution time.