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)!

Embedl SDK

Benefits 

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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.

Read more 

Hand
 
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.

Read more

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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.
 
 
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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.

Plane icon
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.
 
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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.

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.

Industries

Efficient Deep Learning for Embedded Systems in the Automotive Industry
Efficient Deep Learning for Embedded Systems in the IoT Industry
Efficient Deep Learning for Embedded Systems in the Aerospace Industry
Want to find out more?  Our award winning Deep Learning Optimization Engine optimizes your Deep  Learning model for deployment BOOK A DEMO