A Safari through FPGA-based Neural Network Compilation and Design Automation Flows

Abstract

Thanks to the enormous computing power of GPUs, Machine Learning (ML) based on artificial neural networks has found its way into many important application fields. Sophisticated compiler infrastructures facilitate the task of mapping neural networks onto these accelerators. Recently, new developments have also led to compilation and design automation flows that target FPGA-based accelerators. Although not being as mature as their GPU counterparts, there exists a multitude of published and actively developed approaches with differing support levels for network classes, file formats, and target platforms. Neural network exchange file formats advance jointly with the modeling frameworks. In this paper, we take a quick safari through the jungle of neural network compilation flows for FPGA-based targets by reporting qualitative and quantitative metrics. For comparison, we study the classes of supported neural network architectures of each approach, and the corresponding compatibility of exchange formats, emphasizing ONNX, by examining available conversion tools. Besides, we look at several non-functional properties, including FPGA resource utilization and performance numbers for selected neural networks, but also soft criteria such as licensing, community support, and development activity. Finally, we also assess and discuss some deficiencies currently still affecting some approaches. We hope that our study supports interested readers to orient themselves in the jungle of available flows concerning both functionality and usability, as well as to guide further development and research activities in the endeavor of automated ML acceleration on FPGAs.

Topics & Concepts