SoyNet, an inference-specialized acceleration solution for AI models, has been developed to replace existing AI frameworks,
including TensorFlow, PyTorch, and Caffe, and deliver faster inference services.
Korea Patent Application No. : 10-2018-0136437
PCT International Application No.: PCT/KR2018/013795
Intended not for specific AI models but for inference execution, SoyNet can be integrated to run countless AI models.
According to our tests on the Yolo V3 model, SoyNet’s processing speed was 3 times faster and its memory usage 1/9 less than GPU-based TensorFlow.
Performance results may vary depending on specific AI models, but SoyNet, in general, performs 2-5 times faster and uses 1/5-1/9 less memory compared to other accelerators.
Play the below video to compare the execution speed of
an AI model on TensorFlow and SoyNet.
The latest deep learning models introduced by recent research articles can only be executed alone and on a high-end GPU like GTX1080ti,
SoyNet can run a combination of several models unveiled by the latest studies, ultimately expanding the scope of feasible services.
SoyNet’s specialized acceleration solutions reduce our clients’ server costs.
See below for SoyNet’s cost-cutting benefits in an intelligent CCTV architecture.
SoyNet supports over 3 times as many camera channels as before with the same equipment, reducing the cost of installing additional video analysis equipment.
Intelligent CCTVs are fast emerging as a critical element of a smart city.
As the Korean government has successfully completed the Intelligent CCTV Pilot Project, more and more local governments are adopting this innovative security solution.
When an AI-based video analysis server is run on SoyNet, it can process more than 3 times more camera channels than before, considerably lowering server costs.
SoyNet enables you to reduce your reliance on high-cost AI specialists and harness your existing application developers for AI service development.
SoyNet lets you solely focus on your domain model in the development phase and run your service right away in its optimal condition once the model is developed.