Blog
Discover deep learning insights and trends. Read stories from Deci’s platform users. Stay in the know about our product and technology as we make DL tools more accessible to developers across industries.
Featured Blog
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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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.
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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.
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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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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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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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.
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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.
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