Boost Manufacturing Efficiency with Powerful Deep Learning Models

The Challenge

Computer vision-based solutions deliver the intelligence to simplify processes, drive new efficiencies, and empower faster decision-making. However, the inability to run real-time inference, high false alarms due to low model accuracy, and the failure to deploy on CPUs or edge devices are just some of the barriers to production faced by AI teams in manufacturing companies today.

Achieve Real Time Inference to Detect Defects as They Happen

Improve latency and throughput, reduce memory footprint while maintaining the model’s accuracy.

Reduce False Alarms with Better Model Accuracy

Increase inspection and production capacity with better model accuracy.

Eliminate Development Risk and Reach Deployment Faster

Eliminate endless trial and error iterations. Go from data to a production ready model in days.

See It in Action

Watch a compilation of clips demonstrating how you can use Deci in a variety of manufacturing use cases.

Get Similar Results for Your Specific Use Case

Improving Inspection Quality & Production Capacity for a Defect Detection Application

A manufacturing company specializing in materials engineering for the semiconductor industry sought to improve their defect detection inspection process and capacity by accelerating their model’s runtime performance.

The team used the Deci platform and its NAS engine to build a custom segmentation model that delivered a 1.6x increased throughput, a 62% reduction in model size, and a 13% reduction in memory footprint while maintaining the accuracy. The new model was trained on-premise using Deci’s open-source training library called SuperGradients and then compiled and quantized the model to TensorRT FP16 using Deci’s platform.

"Applied Materials is at the forefront of materials engineering solutions and leverages AI to deliver best-in-class products. We have been working with Deci on optimizing the performance of our AI model, and managed to reduce its GPU inference time by 33%. This was done on an architecture that was already optimized. We will continue using the Deci platform to build more powerful AI models to increase our inspection and production capacity with better accuracy and higher throughput."

Amir Bar, Head of SW and Algorithm
Applied Materials

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")