Abstract: Large-scale particle physics experiments face challenging demands for both high-performance trigger systems as well as high-throughput computing resources. The growing exploration of machine learning algorithms in particle physics offers new solutions for simulation, reconstruction, and analysis. These new machine learning solutions often lead to increased parallelization and faster reconstruction times on dedicated hardware, specifically field programmable gate arrays (FPGAs). I will discuss studies exploring the feasibility of FPGA-accelerated machine learning inference as trigger and computing solutions in particle physics.