Provides a Model Optimization SDK that reduces deep learning model memory usage by up to 95% and energy consumption by up to 83%, enabling efficient AI deployment on resource-constrained embedded systems. This technology accelerates inference speeds by up to 18x, helping industries like automotive, aerospace, and IoT develop cost-effective, high-performance AI solutions.
Funding
$6.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Deploying deep learning models on embedded systems is challenging due to the limited memory, processing power, and energy resources of these devices. This often results in increased hardware costs, slower inference speeds, and higher energy consumption, hindering the development of efficient AI solutions for industries like automotive, aerospace, and IoT.
Solution
Embedl offers a Model Optimization SDK designed to reduce the memory footprint of deep learning models by up to 95% and decrease energy consumption by up to 83%, enabling efficient AI deployment on resource-constrained embedded systems. The SDK accelerates inference speeds by up to 18x, allowing developers to create cost-effective, high-performance AI products. Embedl's technology streamlines the development cycle, driving innovation and competitive advantage in industries such as automotive, defense, and emerging IoT sectors. The Embedl Hub provides a platform to compare models and hardware, ensuring performance needs are met and keeping up with trends for efficient edge AI applications.
Target Audience
The primary customers are AI leaders and developers in the automotive, aerospace, and IoT industries who require efficient AI deployment in embedded systems.
Features
- Model Optimization SDK reduces deep learning model memory usage by up to 95%
- Reduces energy consumption by up to 83%
- Accelerates inference speeds by up to 18x on devices
- Integrates smoothly with popular automotive development frameworks and tools
- Streamlines deep learning models for IoT devices, enhancing performance by addressing memory limits, reducing inference times, and cutting power use
- Ensures components meet stringent aerospace standards while accelerating development cycles
- Embedl Hub allows users to stay updated on edge hardware benchmarks to compare models and hardware