Deep Learning in MATLAB: From Concept to Optimized Embedded Code

Wednesday, May 23, 2:10 PM - 2:40 PM
Summit Track: 
Enabling Technologies
Exhibit Hall A-2

Learn how to adopt a MATLAB to design deep learning based vision applications and re-target deployment to embedded CPUs and GPUs. The workflow starts with algorithm design in MATLAB, which enjoys universal appeal among engineers and scientists because of its expressive power and ease of use. The algorithm may employ deep learning networks augmented with traditional computer vision techniques and can be tested and verified within MATLAB.

Next, those networks are trained using MATLAB's GPU and parallel computing support either on the desktop, a local compute cluster, or in the cloud. In the deployment phase, code-generation tools are employed to automatically generate optimized code that can target both embedded GPUs like Jetson TX2, DrivePX2, or Intel based CPU platforms or ARM-based embedded platforms. The generated code is highly optimized to the chosen target platform. The auto-generated code is ~2.5x faster than mxNet, ~5x faster than Caffe2, ~7x on these platforms. We’ll use an example of LiDAR processing for autonomous driving to illustrate these concepts.


Avinash Nehemiah

Product Marketing Manager, Computer Vision, MathWorks

Avinash Nehemiah, product marketing manager for computer vision at MathWorks, has ten years of experience in computer vision. Prior to joining MathWorks he led a team that created a computer vision-based solution for patient safety in hospital rooms. Avinash has a Master’s degree in electrical and computer engineering from Carnegie Mellon University, where his research focused on object recognition in radar imagery.

Girish Venkataramani

Product Development Manager, Mathworks

Girish Venkataramani is product development manager at MathWorks with over 15 years of experience in designing optimizing compilers for embedded systems. At MathWorks, he leads teams that deliver key technical innovation in code-generation products that enable deployment of deep learning and vision algorithms described in MATLAB and Simulink onto embedded hardware platforms like ARM processors, FPGAs, and GPUs. Girish graduated from Carnegie Mellon University with a Ph.D. in electrical and computer engineering. His research interests are in the fields of optimizing compiler design, computer architecture (GPUs, FPGAs, embedded CPUs), computer vision and deep learning deployment.

See you at the Summit! May 20-23 in Santa Clara, California!
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