Real-Time Sensor and Image Analysis with Edge Machine Learning
At Synective Labs, we understand the need for real-time and accurate analysis of sensor and image data. Running machine learning models on the cloud can result in slow response times, making it difficult to perform real-time analysis. To address this challenge, we offer advanced machine learning techniques tailored for edge computing, closer to where the sensor data is being collected and reducing cloud computation costs.
Our expertise in using tools such as PyTorch, TensorFlow and Vitis AI allows us to develop models that can run on the edge, making the analysis of sensor and image data faster and more efficient. By leveraging IoT-to-Cloud communications, we can automate data collection and model training in the cloud, enabling real-time analysis and faster response times
Data collection and processing
Data collection is the first crucial step to any machine learning project. We leverage IoT-to-Cloud communications to automate data collection, making it easier and more efficient.
Optimisation and deployment to edge device
Our solutions utilize cutting-edge tools such as TensorFlow Lite Micro to optimize our trained models for deployment on microcontrollers, while utilizing FPGA-vendor toolchains or hand-coded RTL for FPGA implementations.
The optimized model is deployed to the edge device, where it can run in real-time and provide accurate and efficient analysis of electronic device wear, sensor, and image data.
Contact us to learn how our machine learning techniques can be tailored to improve real-time sensor and image analysis for your application.
We offer services:
Edge Convolutional Neural Networks
Deep Learning on FPGA
Feasibility Studies and Consultation
Hardware Acceleration of Machine Learning Algorithms
Training Data Collection
Synective has developed a demo system to showcase the power of embedded FPGA Deep Learning. It was able to classify hand-written digits using nothing but a tiny, low-end FPGA and a camera.
Synective has designed a CNN that is optimized with respect to memory footprint and inference speed. The network is implemented and runs on the ARM processor of the Raspberry Pi.
Synective continually take on thesis workers that improve our embedded Deep Learning system.