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Acceleration of Image Classification and Object Tracking by the Intel Neural Compute Stick 2 with Power Efficiency
Tianyu Gao1, Jozsef Suto1,2
1Department of Informatics Systems and Networks, Faculty of Informatics, University of Debrecen, Kassai Street 26, 4028 Debrecen, Hungary.
Sensors (Basel, Switzerland)
|April 28, 2025
Summary
The Intel Neural Compute Stick 2 (NCS2) significantly accelerates deep learning on Raspberry Pi 4B, boosting image recognition by 400% and object tracking by up to 1400%. This enables efficient real-time AI in embedded systems.
Area of Science:
- Computer Vision
- Embedded Systems AI
- Hardware Acceleration
Background:
- Real-time operation of complex neural networks is crucial for embedded systems.
- Deploying deep learning in industrial applications requires cost-effective solutions.
- The Intel Neural Compute Stick 2 (NCS2) offers potential for edge AI acceleration.
Purpose of the Study:
- To evaluate the efficiency and power consumption of the NCS2 on a Raspberry Pi 4B.
- To assess the NCS2's performance in accelerating image classification and object tracking.
- To supplement OpenVINO™ documentation with real-world application data.
Main Methods:
- Utilized Raspberry Pi 4B platform with Intel NCS2.
- Conducted image recognition tests using a single model.
- Performed real-time object tracking tests with two models, employing the Deep SORT algorithm.
- Evaluated performance variations with different model numbers and conditions.
Main Results:
- NCS2 increased image recognition performance by approximately 400%.
- Real-time object tracking performance improved by 1200% to 1400%.
- Achieved over 50 FPS for image recognition and over 20 FPS for object tracking.
- Observed 200% to 400% power efficiency gains with NCS2.
Conclusions:
- The NCS2 provides substantial performance enhancements for AI tasks on constrained hardware.
- The combination of Raspberry Pi 4B and NCS2 is effective for real-time embedded AI applications.
- Significant power efficiency improvements make NCS2 a viable solution for edge computing.
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