Develop Superior ADAS Applications with Accelerated Inference Performance

The Challenge

Advanced driver-assistance systems powered by deep learning models are transforming the mobility and transportation industry. However, a common barrier to deployment is the inability to achieve high accuracy and real-time inference performance on edge devices. Both factors are absolutely mission critical for ensuring not only the application’s usability, but also safety for the users.

Enable New Applications on Edge Devices

Improve model accuracy and speed, 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

Ship differentiated products powered by superior models.

See It in Action

Watch a compilation of clips demonstrating how you can use Deci to develop a variety of automotive use cases.

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 latency 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 3x – all while maintaining the original accuracy.

Application Showcase

Deci is used by companies in the automotive industry to develop and optimize a wide range of applications. Here are some examples.

Advanced Driver Assistance Systems

Pothole Detection

Automatic Number Plate Recognition

Occupant Monitoring System

Parking Monitoring

Automotive Manufacturing

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")