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.
Average reduction in Inference Cloud Cost
Boost in Model Accuracy
Average Throughput Acceleration
Average reduction in Model Size
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.
Make the most of your devices and scale up inference more cost efficiently with better hardware utilization.
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Vadim Zhuk, Senior Vice President
RingCentral