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embARC Machine Learning Inference (MLI) Workshop
| Thursday, September 19, 2019 | |
| Santa Clara Marriott
2700 Mission College Blvd, Santa Clara, CA 95054 | |
| Session 1: 11:30 a.m. – 1:30 p.m.
Lunch will be served from 1:30 p.m. – 2:15 p.m. | Session 2: 3:15 p.m. – 5:15 p.m.
Lunch will be served at 1:00 p.m. |
Instructors: Jamie Campbell
Jamie Campbell is a Software Engineering Manager at Synopsys who leads the Embedded Vision Processor Applications Team, responsible for creating interesting demos and reference applications for the Synopsys EV processor. Prior to focusing on embedded vision, Jamie has worked in various capacities as an embedded software specialist, including R&D engineer, Field Applications Engineer and Corporate Applications Engineer at Precise Software Technologies, ARC International, Virage Logic and now Synopsys. Jamie holds a Bachelor of Science in Electrical Engineering from the University of Calgary, Canada.
Anatoly Savchenkov is an R&D Manager at Synopsys and is responsible for embedded software running on ARC cores and subsystems. He came to Synopsys through acquisitions of Virage Logic and ARC International where he had similar roles. Anatoly holds a Master’s degree in computer science from St. Petersburg Polytechnic University in St. Petersburg, Russia.
Dmitry Zakharov is the lead developer of Synopsys embARC Machine Learning Inference Library (MLI). Dmitry started his career in 2013 working on speech synthesis and speech recognition systems. He later joined Synopsys in 2016 and holds a Masters Degree in Computer Science with specialization in embedded systems.
The embARC Machine Learning Inference (MLI) software library is optimized for low-power IoT applications that utilize convolutional neural networks (CNN) and recurrent neural networks (RNN). During this workshop, participants will get hands-on experience using the MLI library on the Synopsys ARC EM processor, by building an application which uses a CNN to recognize hand-written characters.
Instructors will provide step-by-step instructions and “check points” along the way so that everyone can stay on schedule.
Attendees will be able to keep the instructions and the software content will be open-sourced.
Attend this workshop to learn:
• How to implement efficient machine learning “at the edge” using Synopsys’ ARC EM processor
• How Synopsys’ optimized open-source Machine Learning Inference libraries target low-power ARC processors
• How to map neural network graphs defined in the TensorFlow framework to low-power ARC processors
• How to create a real-time character-recognition application that runs on ARC EM SDP hardware
• How to work with the ARC MetaWare Development Toolkit to build and debug applications running on the ARC EV processor
*Attendees should have C/C++ programming experience and some basic knowledge of Python.
In this hands-on workshop, you'll:
• Gain an introductory understanding of the embARC MLI Library and basic usage concepts
• Prepare a character-recognition CNN graph using TensorFlow
• Transform and quantize of the graph into an “MLI-ready” format
• Integrate the model into an embedded application suitable for execution on the ARC EM-based target
• Execute and test the application