Meet with Deci at Embedded Vision Summit
May 22-24, 2023
Santa Clara, California
We’re exhibiting at Embedded Vision Summit on May 22-24 at the Santa Clara Convention Center.
Whether you are looking to achieve real-time performance, reduce model size, or increase throughput, Deci's NAS-based model optimization can help you deliver seamless inference on any edge device.
Book a meeting with Deci’s experts to learn how Deci’s Automated Neural Architecture Construction Engine can empower your team to build highly accurate and efficient models for your edge AI applications.
See you at booth 715!
Don't Miss the Excitement at Booth 715!
See Deci's E2E Deep Learning Platform in Action
- Learn more about how we can help you achieve your specific goals
- Get a walkthrough of Deci’s platform where you can perform the end-to-end deep learning development cycle – from creating your own model and choosing the relevant public dataset, to selecting the right hardware and defining optimization KPIs
Discover the Power of YOLO-NAS, a Model Generated by AutoNAC
See for yourself how YOLO-NAS:
- Runs at unparalleled accuracy and speed, outperforming other well-known models.
- Is fully compatible with high-performance inference engines like NVIDIA® TensorRT™ and supports INT8 quantization.
- Leverages cutting-edge techniques, such as attention mechanisms, quantization aware blocks, and reparametrization at inference time.
- Is easy to fine-tune to achieve SOTA results using Google Colab Notebook with the SuperGradients open-source library.
Talk with Deci's Experts 1-on-1
Get a free consultation session and learn how other leading AI teams are collaborating with Deci to solve edge AI design and deployment challenges
Tomer Keren
Deep Learning Engineer
LinkedIn →
Harpreet Sahota
DevRel Manager
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Mike Flynn
Sales Development Manager
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AutoNAC (Neural Architecture Search Engine)
Automatically find the architecture that delivers the highest accuracy for your specific speed, size, and inference hardware targets
Tailored for Your Task & Performance Goals
Run a multi-objective search to generate an architecture optimized for several parameters (accuracy, throughput, latency, model size, and memory footprint)
Hardware Aware Neural Architecture Search
Take hardware constraints into account to unlock optimal efficiency that hit your performance targets
Outperforms any SOTA Computer Vision Model
Deliver the best accuracy and speed for your specific use case that outperform SOTA open-source neural networks