Given the growth in data inputs and application complexity, it is often the case that a single hardware accelerator is not enough to solve a given problem. In particular, the computational demands and I/O of many tasks in Machine Learning often require a cluster of accelerators to make a relevant difference in performance. In this talk, I present the efficient construction of FPGA clusters using inference over Decision Tree Ensembles as the target application.
Dr. Amit Kulkarni is a Postdoctoral Research Associate within Systems group at ETH university Zurich, Switzerland. He earned a Ph.D. degree in Electrical Engineering from the Ghent University Belgium in 2017. His current research interest includes accelerating machine learning algorithms on FPGAs, accelerating data sketch algorithms on FPGAs and he is currently involved in building the reconfiguration infrastructure for the research computer called "Enzian" ( www.enzian.systems ).