Our Mission

We help customers build extraordinary
Deep Learning based products.

Accelerated  Time-to-Market

Accelerated
Time-to-Market

improved Product margins

improved Product margins

Better Product Specifications

Better Product Specifications

Value Added Partnership

Value Added Partnership

product

Technology

Our award winning Deep Learning Optimization Engine optimizes your Deep Learning model for deployment (inference) to meet your requirements of:

  • Execution Time (Latency)
  • Throughput
  • Runtime Memory Usage
  • Power Consumption

EmbeDL enables you to deploy Deep Learning on less expensive hardware, using less energy and shorten the product development cycle.

EmbeDL interfaces with the commonly used Deep Learning development frameworks, e.g. Tensorflow and Pytorch. EmbeDL also have world leading support for hardware targets including CPUs, GPUs, FPGAs and ASICs from vendors like Nvidia, ARM, Intel and Xilinx.

We are happy to answer any questions and/or demonstrate EmbeDL on your Deep Learning model(s)!

TECHNOLOGY
Want to find out more?  Our award winning Deep Learning Optimization Engine optimizes your Deep  Learning model for deployment Book a Demo

benefits

FASTER EXECUTION

FASTER
EXECUTION

By using state-of-the-art methods for optimizing Deep Neural Networks, we can achieve a significant decrease in execution time and help you reach your real time requirements.

SMALLER FOOTPRINT IN DEVICE

SMALLER
FOOTPRINT IN DEVICE

The EmbeDL Optimization Engine automatically reduces the number of weights , and thus size of the model, to make it suitable to be deployed to resource constraint environments such as embedded systems

SHORTER TIME-TO-MARKET

SHORTER
TIME-TO-MARKET

The tools are fully automatic, which reduces the need for time consuming experimentation and thus shorter time-to-market. It also frees up your data scientists to focus on their core problems.

LESS ENERGY USAGE

LESS
ENERGY USAGE

Energy is a scarce resource in embedded systems and our optimizer can achieve an order of magnitude reduction in energy consumption for the Deep Learning model execution.

IMPROVED PRODUCT MARGINS

IMPROVED
PRODUCT MARGINS

By optimizing the Deep Learning model, cheaper hardware can be sourced that still meets your system requirements leading to improved product margins.

DECREASED PROJECT RISK

DECREASED
PROJECT RISK

Optimizing and deploying our customers’ Deep Learning models to embedded systems is what we do. By outsourcing this to us, your team can then focus on your core problems.

Partners and customers

Zenseact
(Volvo Cars)

Veoneer

Chalmers University

Bielefeld University

Technion – Israel Institute of Technology

Siemens

Christmann Informationstechnik

Barcelona Supercomputing Center

Osnabrück University

Research Institutes of Sweden

Antmicro

Maxeler Technologies

Gothenburg University

Neuchatel University

Technisch Universität Dresden

Networks
We are humbled to have been accepted into the following networks bringing together industry, academia and startups.
Hipeac
Mobilityxlab
Aiinnovation of sweden
AWARDS
The EmbeDL Optimization Engine has received several awards for its technology and implementation.
Hipeac
IVA
LABEL

Latest News

EMBEDL IN NEW €8M EU PROJECT!

EMBEDL IN NEW €8M EU PROJECT!

EMBEDL TO DEVELOP NEXT-GENERATION EFFICIENT AI PLATFORM IN VEDLIOT Autonomous vehicles and devices for intelligent homes are becoming increasingly complex. These applications  involve large collaborative systems which are powered  by AI-based algorithms distributed...

Latest Blog

The 4 main challenges of deploying deep learning

The 4 main challenges of deploying deep learning

  Are you experienced in making AI powered products or is this your first time on the deep learning carousel? Either way, you will face the same obstacles. No matter which hardware you are running on, there are always budget constraints to consider. Whether it´s a power budget, execution time budget or just a maximum cost for hardware that needs to be satisfied....

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 780681.