Use Cases

Image Recognition Systems

Driving Sophisticated Computer Vision with Machine Learning

One of the most powerful applications of machine learning and computer vision is image recognition. While it is trivial for a human being to recognize, categorize, or differentiate between images, the sheer volume of data encoded in a given image—especially one at a very high resolution—has until quite recently made computer-powered image recognition very challenging. 

Today, image recognition systems powered by Neural Magic’s Inference Engine can be used to develop sophisticated computer vision applications for industries and use cases ranging from industrial manufacturing to retail to media—helping companies achieve previously impossible feats.

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Benchmark
Resnet 50
Better real-time inference price performance than Volta GPU at fp32

Bringing Computer Vision into its Own

Today, when machine learning engineers run image recognition models on a CPU, they often make sacrifices that affect the quality of outcomes, because they are forced to make untenable sacrifices between:

  • Performance
  • Batch Size 
  • Accuracy
  • Cost

Neural Magic addresses these limitations by generating GPU-class performance on a CPU.

Key Benefits
Software Flexibility
Bring computer vision to general-purpose CPUs. Run models anywhere: on premise, in the cloud, or at the edge.
Seamless Integration
Integrate seamlessly into existing CI/CD pipelines. Deploy in containers or virtual machines, and manage with Kubernetes like any modern software application.
Better Price Performance
High-performance algorithms that can run AI on commodity machines via smarter CPU memory utilization.

Neural Magic In The News

Neural Magic Announces $15 Million in Seed Funding
The seed investment is led by Comcast Ventures, and including NEA, Andreessen Horowitz, Pillar VC and Amdocs
Bryan House
11/06/19
Announcing the Neural Magic Inference Engine
We are proud to announce the first version of the Neural Magic Inference Engine, offering GPU-class performance on commodity CPUs.
Bryan House
11/07/19
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Gaurav Rao
11/05/19