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.

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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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    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.

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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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    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.

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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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    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.

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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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    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.

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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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    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.

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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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    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.

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    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.

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