Low Power CV - Industrial Automation, Low power microcontroller based ML
Low Power CV - Industrial Automation, Low power microcontroller based ML
Low Power CV
What is Low Power CV ?
- Edge or Mobile Computer Vision
- Microcontroller based Computer Vision
Edge or Mobile Computer Vision
Frameworks: Linux, Assembly Level, RISC-OS (V)
Power Ranges: 5V - 24 V DC , 1-8 A
Libraries and Deployment: Most of the deep learning libraries and deployment frameworks
Programming Languages : Python, C++
- Use of Onboard Edge Devices in multiple fields now a days is most common.
- Some of the famous edge devices are:
- Jetson Nano
- Jetson Xavier AGX
- Jetson Xavier NX
- Google CoralTPU USB Adapter
- Google Coral Board
- Raspberry Pi
- Beagle Bone
- Panda Board
- Asus Boards etc.
- Edge Devices can be connected to SDKs and can be used in different deployment methods to optimise costs.
- AWS GreenGrass
- GCP IOT
- Azure IOT
- With the help of open-source SDK’s
- Some of them are:
- Kafka
- Kubernetes
- ROS
- Some of them are:
Deployment Frameworks:
- Tensorflow Lite
- Triton models (.trt, .engine)
- OpenVino IR Models
Microcontroller based Computer Vision (Embedded Computer Vision)
Power Ranges: 3.3 - 9V DC, 0.1 - 2 A
Programming Languages : Micropython , C++ , Assembly C
Libraries: TFLite, TinyML- Formats any, Open Vino (Still under experimentation)
Frameworks: Linux, Assembly Level, RISC-OS (V)
What are these devices for?
These are basically very low powered devices used for mostly Single task based Computer Vision in a more efficient manner and consuming as less current as possible.
- Sipeed MAIX Go Suit
- Open MV M7 —
- Open MV H7 Plus —-
- Open CV AI Kit
- Tiny Vision based FPGA Board (FPGA - Field Programmable Gate Arrays) etc.
These devices are less costly and low maintenance too as they don’t need extra efforts to cool the systems and also to power the systems.