Deep Neural Networks (DNNs) are computation intensive. Without efficient hardware implementation of DNNs, many promising IoT (Internet of Things) applications will not be practically realizable. In this talk, we will first take a detailed look into one type of compute accelerators, FPGA, and evaluate its potential role in the upcoming IoT revolution. Although FPGAs can provide desirable customized hardware solutions, they are difficult to program and optimize. High-level synthesis is an effective design flow for FPGAs due to improved productivity, debugging, and design space exploration abilities. However, optimizing large DNNs under resource constraints for FPGAs is still a key challenge. We will present a series of effective design techniques for implementing DNNs on FPGAs with high performance and energy efficiency. These include the use of configurable DNN IPs, resource allocation across DNN layers, Winograd and FFT techniques, and DNN reduction and re-training. We showcase several design solutions including Long-term Recurrent Convolution Network (LRCN) for video captioning, Inception module (GoogleNet) for face recognition, as well as Long Short-Term Memory (LSTM) for sound recognition. We will also present some of our recent work on developing new DNN models and data structures for achieving higher accuracy for several interesting applications such as crowd counting, genomics, and music modeling.
Dr. Deming Chen obtained his BS in computer science from University of Pittsburgh, Pennsylvania in 1995, and his MS and PhD in computer science from University of California at Los Angeles in 2001 and 2005 respectively. He joined the ECE department of University of Illinois at Urbana-Champaign (UIUC) in 2005 and has been a full professor in the same department since 2015. His current research interests include system-level and high-level synthesis, machine learning, computational genomics, GPU and reconfigurable computing, and hardware security. He has given about 100 invited talks sharing these research results worldwide. Dr. Chen is a technical committee member for a series of top conferences and symposia on EDA, FPGA, low-power design, and VLSI systems design. He is an associated editor for several leading IEEE and ACM journals. He received the NSF CAREER Award in 2008, the ACM SIGDA Outstanding New Faculty Award in 2010, and IBM Faculty Award in 2014 and 2015. He also received six Best Paper Awards and the First Place Winner Award of DAC International Hardware Contest on IoT in 2017. He is included in the List of Teachers Ranked as Excellent in 2008 and 2017. He was involved in two startup companies previously, which were both acquired. In 2016, he co-founded a new startup, Inspirit IoT, Inc., for design and synthesis for machine learning targeting the IoT industry. He is the Donald Biggar Willett Faculty Scholar of College of Engineering of UIUC.