The recent boom in artificial intelligence (AI) alongside big data being collected everywhere in our world, is impacting our lives in an unprecedented way. The growing exploration of machine learning algorithms in particle physics offer new solutions in many areas such as analyses, simulation and reconstruction. However, the large number of computations also make it extremely challenging to program these algorithms on more energy-efficient, faster computing hardware such as field programming gate arrays (FPGAs) or application-specific customized chips (ASIC), thus restricts the usage of these networks in applications that have stringent speed requirements, such as in large-scale particle physics experiments, which need to handle large data volume at a high rate. Accelerated machine learning inference can potentially provide solutions to these challenging demands for both high-performance trigger as well as high-throughput computing systems. In my talk, I will discuss developments in employing accelerated machine learning inferences as solutions to our data processing challenges in particle physics.