Research Opportunities in Heterogeneous Computing for Machine Learning

In recent times, AI and deep learning have witnessed explosive growth in almost every subject involving data. Complex data analyses problems that took prolonged periods, or required laborious manual effort, are now being tackled through AI and deep-learning techniques with unprecedented accuracy. Machine learning (ML) using Convolutional Neural Networks (CNNs) has shown great promise for such applications. However, traditional CPU-based sequential computing no longer can meet the requirements of mission- critical applications which are compute-intensive and require low latency. Heterogeneous computing (HGC), with CPUs integrated with accelerators such as GPUs and FPGAs, offers unique capabilities to accelerate CNNs. In this presentation, we will focus on using FPGA-based reconfigurable computing to accelerate various aspects of CNN. We will begin with the current state of the art in using FPGAs for CNN acceleration, followed by the related R&D activities (outlined below) in the SHREC* Center at the University of Florida, based on which we will discuss the opportunities in heterogeneous computing for machine learning.