Run Real-Time Inference at the Edge

Improve latency and throughput and reduce model size by up to 10x while maintaining your models’ accuracy.

Deep learning can be challenging under the best of circumstances, but deep learning
on edge devices carries extra complexity because of the constrained environment.
Edge hardware devices simply don’t have the flexibility you get in cloud machines
when it comes to OS, drivers, compute resources, memory, testing, and tuning. Failing
to adapt to this environment can lead to delays in deployment. Hence, it’s important
to be aware of the specific requirements as well as potential challenges that can
arise.

85%

Average reduction in Inference Cloud Cost

1%

Boost in Model Accuracy

4x

Average Throughput Acceleration

62%

Average reduction in Model Size

Enable New Applications on Edge Devices

Improve model inference and reduce model size and memory footprint to run on resource constrained devices (e.g. mobile, laptops, cameras, etc.) without compromising on accuracy.