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Case Study

Embedded DNN

PROJECT
Embedded DNN
INDUSTRY
Consumer Electronics
SERVICE
Convolutional Neural Net
The challenge was to create a Convolutional Neural Net that would run on a constrained system.
PROCESS
The process was to first prototype a CNN running in real-time performing image recognition on a desktop class device. From there optimize the architecture, primarily the models so that they could run on an embedded device.
RESULT
Cross platform library that runs in real-time at greater then 25fps that performs image recognition on known datasets at >90% accuracy.
TECHNICAL DETAIL
TensorRT, C++
Initially running on ARM Cortex A-series
Currently working on M-series

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