A High Level Synthesis Methodology for Dynamic Monitoring of FPGA ML Accelerators

Ryan F. Forelli, Rui Shi, Seda Ogrenci, Joshua Agar

Abstract: In this paper, we present concepts towards a HLS-driven dynamic monitoring and debugging framework. Traditionally, in-situ debugging and dynamic monitoring is accessible during the early design stages through costly co-simulation cycles and through invasive tools and interfaces. We propose a methodology where dynamic monitoring is embedded into the high level synthesis description of machine learning (ML) accelerators within the open source hls4ml tool. We discuss the usage of the framework for monitoring FIFO channel utilization, which is a critical structure utilized to implement streaming based ML accelerators on FPGAs.