Run Real-Time Inference at the Edge

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

Enable New Applications on Edge Devices

Improve model inference and reduce model size and memory footprint to run on resource constrained devices without compromising on accuracy.

Scale up Inference on Existing Edge Devices

Make the most of your devices and scale up inference more cost efficiently with better hardware utilization.

Migrate Workloads from
Cloud to Edge

Enables new applications on edge devices with smaller and more efficient models.

Migrate Inference Workloads From Cloud to Edge

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Discover Tips to Accelerate Inference Performance of Your AI Applications

Get Similar Results for Your Specific Use Case

Enabling Real-Time Semantic Segmentation for an Automotive Application

An automotive company running a U-Net based segmentation model on a NVIDIA Jetson Xavier NX struggled to achieve the target throughput in production.

Using Deci’s AutoNAC engine, a faster and smaller model was generated. Latency was reduced by 2.1X, model size was reduced by 3X, and memory footprint was reduced by 67% – all while maintaining the original accuracy.

See How It Works

Watch a quick walkthrough of how you can use Deci to accelerate your models’ inference performance. 

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“At RingCentral, we strive to provide our customers with the best AI-based experiences. With Deci’s platform, we were able to exceed our deep learning performance goals while shortening our development cycles. Working with Deci allows us to launch superior products faster.”

Vadim Zhuk, Senior Vice President
RingCentral

The Ultimate Guide to Inference Acceleration of Deep Learning-Based Applications

Learn 12 inference acceleration techniques that you can immediately implement to improve the speed, efficiency, and accuracy of your existing AI models.