Programmers’ Introduction to the Intel® FPGA Deep Learning Acceleration Suite (ODLAINTRO)
Course Description
The Intel® FPGA Deep Learning Acceleration (DLA) Suite provides users with the tools and optimized architectures to accelerate inference using a variety of today’s common Convolutional Neural Network (CNN) topologies with Intel® FPGAs. The Intel® FPGA DLA Suite, included as part of OpenVINO™ toolkit, also makes it easy to write software that targets FPGA for machine learning inference. In this training, we will discuss the advantages of using FPGAs for CNN inference tasks. We will cover the contents of the Intel® FPGA DLA Suite and how various CNN layers are executed on the FPGA using the Intel® FPGA DLA Suite.At Course Completion
You will be able to:
- Understand why FPGAs can be effectively deployed for CNN inference tasks
- Know the contents of the Intel® FPGA Deep Learning Acceleration Suite
- Describe how CNNs are accelerated using the Intel® DLA Suite on the FPGA
Skills Required
- Familiarity with Convolutional Neural Network concepts
Follow-on Courses
Upon completing this course, we recommend the following courses (in no particular order):
- Application Development on the Acceleration Stack for Intel® Xeon® CPU with FPGAs
- Deploying Intel® FPGAs for Deep Learning Inferencing with OpenVINO™ Toolkit
- Introduction to High-Level Synthesis (Part 1 of 7)
- Introduction to High-Level Synthesis with Intel® FPGAs
- Introduction to OpenCL™ on FPGAs for Parallel Programmers
- Introduction to OpenCL™ Programs for Intel® FPGAs
- Introduction to Parallel Computing with OpenCL™ Programs on FPGAs
- Introduction to the Acceleration Stack for Intel® Xeon® CPU with FPGAs
- OpenCL™ Development with the Acceleration Stack for Intel® Xeon® CPU with FPGA
Applicable Training Curriculum
This course is part of the following Intel FPGA training curriculum: