Abstract – Convolutional Neural Networks (CNNs) have led to remarkable breakthroughs in a number of important pattern recognition tasks, such as image and voice recognition. Modern CNNs are composed of many layers, have millions (sometimes billions) of parameters, and require massive amount of computation to train them. The training is typically performed using large amount of data on massively-parallel servers equipped with graphics processing units (GPUs). Subsequently, the trained models are utilized in many application scenarios to run 'inference' on user's data, often on resourceconstrained mobile devices. The computational demand of running modern CNNs creates interesting technical challenges, in particular as it relates to deployment of trained models to resourceconstrained devices, such as smart phones and mobile robots. This talk presents an overview of this emerging area. We will also discuss some ongoing work in our group, where we focus on optimizing CNN inference via custom FPGA-based accelerators or mobile graphics processing units (mobile GPUs).

Dr. Soheil Ghiasi is a professor of electrical and computer engineering at the University of California, Davis. His research interests are design and optimization of embedded systems for signal processing, machine learning and multimedia applications. He received his B.S. degree from Sharif University of Technology, Tehran, Iran in 1998, and his M.S. and Ph.D. in Computer Science from University of California, Los Angeles in 2002 and 2004, respectively. He has served on the organizing and technical program committees of numerous technical conferences, and several journals in the general area of computing systems. He is a senior member of IEEE and ACM.

Abstract – With recent development in Internet of Things (IoT), several environmental researchers, scientists, Government and Corporates initiated monitoring the environmental parameters. The system is made up of three layers such as sensor layer, Hardware layer and Cloud layer. It makes it possible to sense, acquire, process and transfer environmental information over a large area in real time. With today’s lecture, the goal is to demonstrate few of the environmental sensors such as temperature, humidity, air quality, CO2, light and pressure sensors interfacing with ARM Mbed microcontroller (MCU) and transferring into the Amazon Web IoT services. Once the Environmental information is available at the cloud, we could monitor the data on mobile phone as well on the desktop/Laptop computers. One could provide the data analytics to end users utilizing analytics tools offered by various Cloud providers such as IBM Bluemix, AWS IoT, Microsoft Azure, and GE-Predix. The lecture session helps attendees to learn these IoT technologies and services.

Mr. Shivakumar Mathapathi has over 25 years of experience in product development, design and faculty. Mr. Mathapathi is a seasoned technologist, entrepreneur, instructor and practitioner on the Internet of Things (IoT) with extensive experience as lead faculty, lab-practice and mentorship in executing smart city, smart agriculture, assisted living and other IoT related projects. He has designed study programs and academic syllabus for “The IoT course” a 4- units Master of Science course taught at Santa Clara University and CA Polytechnic State University. He led the capstone design project at Cal Poly San Luis Obispo to design and develop IoT cloud platform. He is the Capstone Lecturer at MS Information System, Smart City project, Leavey School of Business, Santa Clara University, and served as lead instructor for IoT course at Cal Poly, San Luis Obispo.