Embedl

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Embedl is a Coding & Development tool that streamlines Edge AI development, offering on-premise and cloud solutions. It optimizes performance and reduces costs for AI models deployed on various edge hardware.

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At a glance

Pricing

Likely Not Free

Free tier

API

Skill level

Technical

About

What is Embedl?

Embedl provides a comprehensive platform for developing and deploying efficient Edge AI. It offers both on-premise and cloud solutions tailored for Edge AI developers, focusing on optimizing performance and reducing costs. The platform includes Embedl Hub, a secure MLOps solution for compliant edge AI workflows, and Embedl Models, which provides popular models optimized for specific edge hardware. Embedl Deploy facilitates Edge AI conversion, compilation, and quantization to get models running on hardware easily. It supports a wide range of hardware platforms including Xilinx FPGAs, Nvidia GPUs, Texas Instruments DSPs, ARM CPUs, NXP NPUs, and Intel CPUs, GPUs, and FPGAs, and is compatible with any inference engine. The Embedl Model Optimization SDK helps developers prune, quantize, and compress models, significantly reducing model size and speeding up inference times.

Best used for

Ideal for developers who need to efficiently develop, optimize, and deploy AI models on edge hardware, streamline MLOps workflows, and ensure compliance. Especially valuable for industries like automotive, defense, and robotics requiring high-performance and cost-effective embedded intelligence.

Common actions

optimize AI models

deploy edge AI

streamline MLOps

quantize deep learning models

benchmark AI performance

Capabilities

Key features

Secure MLOps platform
Optimized edge AI models
Model conversion and quantization
Hardware-aware compilation
Performance benchmarking
Supports various hardware targets
Python SDK for optimization

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Likely Not Free

Not publicly disclosed. Check embedl.com for current pricing.

FAQs

What hardware platforms does Embedl support for model deployment?

Embedl supports a wide variety of hardware platforms for deep learning model deployment, including Xilinx FPGAs, Nvidia GPUs, Texas Instruments DSPs, ARM CPUs, NXP NPUs, and Intel CPUs, GPUs, and FPGAs. They aim to add support for additional hardware targets upon customer request.

Can Embedl optimize models for custom inference engines?

Yes, Embedl is designed to be compatible with any inference engine. This means you can use Embedl to optimize your deep learning models even if your company has developed its own proprietary inference engine.

What is included in the Embedl Model Optimization SDK?

The Embedl SDK provides tools and resources for optimizing deep learning models, including pre-built algorithms for pruning, quantizing, and compressing models. It also features unique visualization tools to track changes during optimization, helping developers improve model efficiency and accuracy.

Does Embedl support specific Python versions for its SDK?

Embedl recommends using stable, maintained versions of Python for its SDK. While they strive for compatibility, there might be delays in supporting the very latest Python versions if they are not yet compatible with frameworks like TensorFlow or PyTorch. They also support Python versions still receiving security fixes.

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Subcategories trending in Coding & Development

Open Source & Models Code Assistants No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

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AI Agents & Automation › AI Frameworks & Infra Content & Design › Product & Industrial Design

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