Deliver Superior AI-Powered
Solutions Faster

The Challenge

Implementing deep learning on edge devices can revolutionize security applications. However, limited computational resources and power constraints pose challenges for running complex deep learning models in real-time on resource-constrained devices.

Enable New Applications on Edge Devices

Improve model inference and reduce model size and memory footprint to run on resource constrained devices.

Scale up Inference on Existing Edge Devices

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

Improve User Experience with Better Inference Speed

Boost inference speed and achieve real-time performance without compromising on accuracy.

How Companies in the Public Sector are Leveraging Deci

Achieve Real Time Inference on the Edge & Maximize HW Utilization

A defense company developing electro-optics solutions for space, airborne, ground, and maritime applications was looking to improve the throughput of an image-denoising model for video stream analysis to deliver real-time insights on the edge. The team was also looking for a way to free up GPU resources to support additional parallel tasks on the same edge device.

Using the Deci platform and its NAS-based engine, the team built a new architecture that delivered a 1.58x acceleration of throughput while maintaining the original model’s accuracy. The team achieved the desired performance within 10 days. Once the team trained the model, they easily compiled and quantized it to TensorRT FP16 using Deci’s platform.

"Deci's AutoNAC engine quickly produced a new model architecture that surpassed our optimized model - a model we’d been working on for 18 months prior. The development time and cost saved is remarkable and provides a significant advantage for our business. Moving forward, we intend to utilize Deci for every new model we develop."

Computer Vision & Deep Learning Team Lead
Large Security Company

Build Better Models Faster with Deci’s Deep Learning Development Platform

Main Capabilities Overview

The Deci platform is used by data scientists and machine learning engineers to build, optimize, and deploy highly accurate and efficient models to production. Teams can easily develop production grade models and gain unparalleled accuracy and speed tailored for any performance targets and hardware environment. Deci is powered by AutoNAC (Automated Neural Architecture Construction), the most advanced and commercially scalable Neural Architecture Search engine in the market. AutoNAC performs a multi-constraints search to find the architecture that delivers the highest accuracy for any performance targets and hardware environment. 

Gain Superior Performance with Custom Architectures

Build accurate & efficient architectures tailored to your hardware and application’s performance targets with Deci’s Neural Architecture Search engine.

Simplify Runtime Optimization

Easily compile and quantize your models (FP16/INT8) and evaluate different production settings with a click of a button.

Maximize Accuracy with Advanced Training Techniques

Train models with SuperGradients. Leverage custom recipes and advanced training techniques (e.g. knowledge distillation, quantization-aware training) with one line of code.

Streamline Deployment with 3 Lines of Code

Deploy your models with Infery, Deci’s simple-to-use, unified, model inference API. Streamline deployment and boost serving performance with parallelism and concurrent execution. Compatible with multiple frameworks and hardware.

Add Your Heading Text Here
					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")