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How to Convert a PyTorch Model to TensorRT™ and Deploy it in 10 Minutes

How to Find the Best Hardware for Deep Learning Model Inference

Finding the right hardware for your model inference can be a daunting task, here are 4 key parameters to consider when you select hardware for inference.

Intel and Deci Collaboration: Deci Breaks the AI Barrier (Again)

After the joint submission to MLPerf, Intel and Deci collaborated again to accelerate three off-the-shelf models: ResNet-50, ResNeXt101, and SSD MobileNet V1.

Exciting News: Deci Raises $21 Million in a Series A Funding Round

Join us on our journey towards making AI efficient, accessible, and scalable for all organizations.

Make the Most of Your Jetson’s Computing Power for Machine Learning Inference

The boost in inference speed of your Deep Learning model compared to your current runs can be as much as 8 times. By the end of this article, you’ll know how to apply it to your use case with minimal effort.

5 Factors that Impact the Inference Pipeline in Production + Hardware Usage Metrics

To achieve optimal performance, the entire inference pipeline in production needs to be fast—not only the model. Here are 5 ways to optimize it.

DeciNets: How Deci’s AutoNAC Automatically Extended the Efficient Frontier of Accuracy-Latency for NVIDIA Cloud and Edge GPUs

Learn how AutoNAC creates proprietary DeciNet architectures that extend the efficient frontiers of latency/accuracy tradeoffs on NVIDIA cloud and edge GPUs.

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Infery – Run Inference for Optimized Models with Only 3 Lines of Python Code

Infery is a Python runtime engine that transforms running inference on optimized models into a light and easy process.

The Object Detection Landscape: Accuracy vs Run Time

Learn about the different types and technical implementations of object detection algorithms, a key domain of deep learning and computer vision.

Podcast: Key Benefits of Using an Inference Acceleration Platform

Listen to this episode of The Data Exchange Podcast to know the reasons why you should optimize your deep learning inference platform.

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How to Convert a PyTorch Model to ONNX in 5 Minutes

Learn about ONNX and how to convert a ResNet-50 model to ONNX. Then, try optimizing it to reduce its latency and increase its throughput.

Graph Compilers for Deep Learning: Definition, Pros & Cons, and Popular Examples

Let’s take deeper dive into how graph compilers work to better understand how, when used correctly, they can offer enormous amounts of acceleration.

How to Automatically Double YOLOv5 Performance in Less Than 15 Minutes

In this post, you'll learn how the Deci platform can optimize your machine learning models. We use the YOLOv5 in our example, but the platform allows you to optimize any model.

The Future of Video Conferencing Looks Promising with AI

Remote working trends accelerated, prompting a surge in demand for video conferencing apps and innovative communication techniques.

How Deci and Intel Hit 11.8x Inference Acceleration at MLPerf

MLPerf provides fair and standardized benchmarks for measuring inference performance of machine and deep learning. Our submission to MLPerf proved that our AutoNAC technology reduces runtime while preserving the accuracy of the base model.

The Next Innovation Wave: AI Building Better AI

One of the reasons why most businesses struggle to obtain real value from AI is the high algorithmic complexity of deep learning models.

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Deci’s Deep Learning Acceleration Platform is Now Available

Take your models to production faster than ever. The platform combines multiple algorithmic and software techniques in an automatic and managed manner.

Toward Defining AutoML for Deep Learning

To date, there is no widely-accepted definition of AutoML, and the industry is using this term to refer to many different things.

Hardware for Deep Learning Inference: How to Choose the Best One for Your Scenario

Deep neural networks have become common practice in many machine learning applications.

An Introduction to the Inference Stack and Inference Acceleration Techniques

Deep neural networks can achieve state-of-the-art performance on many machine learning tasks.

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Product

Deci RTiC – The Case for Containerization of AI Inference

One of the main obstacles impeding the effective utilization of AI models, is their deployment on compute resources, within their target environments.

The Correct Way to Measure Inference Time of Deep Neural Networks

Accurately measuring the inference time of neural networks is not as trivial as it sounds. In this post, we review some of the main issues that should be addressed to measure latency time correctly.

AI Holds the Key to Even Better AI

65% of organizations that have invested in AI in recent years haven’t yet seen any tangible gains from those investments.

Are All FLOPs Created Equal? A Comparison of FLOPs vs Run-Time

Analyses of efficient models often assume that FLOPs can serve as an accurate proxy for efficiency, including run-time. But this is wrong.

Efficient Inference in Deep Learning – Where is the Problem?

Convolutional neural networks can provide superior solutions for many computer vision tasks such as image classification, segmentation, and detection.

The New Electricity Requires Faster Wiring

We’re in the middle of a vast technological revolution, which is transforming life as effectively as the industrial revolution did in the 19th century.

Featured Blog

DeciNets: How Deci’s AutoNAC Automatically Extended the Efficient Frontier of Accuracy-Latency for NVIDIA Cloud and Edge GPUs

Infery – Run Inference for Optimized Models with Only 3 Lines of Python Code

How to Convert a PyTorch Model to TensorRT™ and Deploy it in 10 Minutes

How to Find the Best Hardware for Deep Learning Model Inference

Intel and Deci Collaboration: Deci Breaks the AI Barrier (Again)

Exciting News: Deci Raises $21 Million in a Series A Funding Round

Make the Most of Your Jetson’s Computing Power for Machine Learning Inference

5 Factors that Impact the Inference Pipeline in Production + Hardware Usage Metrics

DeciNets: How Deci’s AutoNAC Automatically Extended the Efficient Frontier of Accuracy-Latency for NVIDIA Cloud and Edge GPUs

Infery – Run Inference for Optimized Models with Only 3 Lines of Python Code

The Object Detection Landscape: Accuracy vs Run Time

Podcast: Key Benefits of Using an Inference Acceleration Platform

How to Convert a PyTorch Model to ONNX in 5 Minutes

Graph Compilers for Deep Learning: Definition, Pros & Cons, and Popular Examples

How to Automatically Double YOLOv5 Performance in Less Than 15 Minutes

The Future of Video Conferencing Looks Promising with AI

How Deci and Intel Hit 11.8x Inference Acceleration at MLPerf

The Next Innovation Wave: AI Building Better AI

Deci’s Deep Learning Acceleration Platform is Now Available

Toward Defining AutoML for Deep Learning

Hardware for Deep Learning Inference: How to Choose the Best One for Your Scenario

An Introduction to the Inference Stack and Inference Acceleration Techniques

Deci RTiC – The Case for Containerization of AI Inference

The Correct Way to Measure Inference Time of Deep Neural Networks

AI Holds the Key to Even Better AI

Are All FLOPs Created Equal? A Comparison of FLOPs vs Run-Time

Efficient Inference in Deep Learning – Where is the Problem?

The New Electricity Requires Faster Wiring

Unleash Your
Deep Learning Models

Blog

Featured Blog

DeciNets: How Deci’s AutoNAC Automatically Extended the Efficient Frontier of Accuracy-Latency for NVIDIA Cloud and Edge GPUs

Infery – Run Inference for Optimized Models with Only 3 Lines of Python Code

How to Convert a PyTorch Model to TensorRT™ and Deploy it in 10 Minutes

How to Find the Best Hardware for Deep Learning Model Inference

Intel and Deci Collaboration: Deci Breaks the AI Barrier (Again)

Exciting News: Deci Raises $21 Million in a Series A Funding Round

Make the Most of Your Jetson’s Computing Power for Machine Learning Inference

5 Factors that Impact the Inference Pipeline in Production + Hardware Usage Metrics

DeciNets: How Deci’s AutoNAC Automatically Extended the Efficient Frontier of Accuracy-Latency for NVIDIA Cloud and Edge GPUs

Infery – Run Inference for Optimized Models with Only 3 Lines of Python Code

The Object Detection Landscape: Accuracy vs Run Time

Podcast: Key Benefits of Using an Inference Acceleration Platform

How to Convert a PyTorch Model to ONNX in 5 Minutes

Graph Compilers for Deep Learning: Definition, Pros & Cons, and Popular Examples

How to Automatically Double YOLOv5 Performance in Less Than 15 Minutes

The Future of Video Conferencing Looks Promising with AI

How Deci and Intel Hit 11.8x Inference Acceleration at MLPerf

The Next Innovation Wave: AI Building Better AI

Deci’s Deep Learning Acceleration Platform is Now Available

Toward Defining AutoML for Deep Learning

Hardware for Deep Learning Inference: How to Choose the Best One for Your Scenario

An Introduction to the Inference Stack and Inference Acceleration Techniques

Deci RTiC – The Case for Containerization of AI Inference

The Correct Way to Measure Inference Time of Deep Neural Networks

AI Holds the Key to Even Better AI

Are All FLOPs Created Equal? A Comparison of FLOPs vs Run-Time

Efficient Inference in Deep Learning – Where is the Problem?

The New Electricity Requires Faster Wiring

Unleash Your Deep Learning Models

Unleash Your
Deep Learning Models