For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/06/processing-raw-images-efficiently-on-the-max78000-neural-network-accelerator-a-presentation-from-analog-devices/
Gorkem Ulkar, Principal ML Engineer at Analog Devices, presents the “Processing Raw Images Efficiently on the MAX78000 Neural Network Accelerator” tutorial at the May 2023 Embedded Vision Summit.
In this talk, Ulkar presents alternative and more efficient methods of processing raw camera images using neural network accelerators. He begins by introducing Analog Devices’ convolutional neural network accelerator, MAX78000, and showing how it achieves superior performance and energy efficiency on a range of neural network inference tasks.
In visual AI applications, cameras provide raw images not in the familiar RGB format, but in a Bayer format. In order to process these images using a neural network that was trained on RGB data, the camera images must be “de-Bayerized” to turn them into RGB images. The conventional way of performing this step is via interpolation. Unfortunately, this increases energy consumption and latency of the application since it cannot be performed by neural network accelerators. Ulkar presents alternative methods of performing this task using neural network accelerators and demonstrates the effectiveness of these techniques.
UiPath manufacturing technology benefits and AI overview
“Processing Raw Images Efficiently on the MAX78000 Neural Network Accelerator,” a Presentation from Analog Devices
1. Processing Raw Images
Efficiently with the
MAX78000 AI Neural
Network Accelerator
Mehmet Gorkem Ulkar
Principal Engineer, Machine Learning
Analog Devices
3. Keep Your Data Close: The Physics of Data
Sources:
• Rick Zarr, TI, 2008, The True Cost of an Internet “Click” - estimate of transfer cost for 30KB page from server http://energyzarr.typepad.com/energyzarrnationalcom/2008/08/the-true-cost-o.html
• J Kunkel et al, University of Hamburg 2010,Collecting Energy Consumption of Scientific Data
• Horowitz ISSCC 2014, 1300-2600 pJ per 64b access
• Chris Rowen, Cadence Design Systems, January 2016, Get Real! – Neural Network Technology for Embedded Systems
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Distance (m)
Credit: Cadence
623
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~1000 km
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Best
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benchmark of novel Edge AI platforms and milliwatt microcontrollers Michele MAGNO, Head
of the Project-based learning Center, ETH Zurich, D-ITET, EMEA TinyML Talks June 2021
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