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IoT

Deep Learning in the IoT Industry

HOW EMBEDL'S MODEL OPTIMIZATION SDK IS REVOLUTIONIZING THE IoT INDUSTRY

The Internet of Things is such a wide industry that incorporates everything from smart ovens, to surveillance cameras, defibrillators and lightbulbs to sensors in the agriculture and manufacturing industries as well as sensors floating around in the oceans or measuring the air quality in our buildings.

No matter the use case there is always a strive in the industry to make these devices recourse efficient and inexpensive to produce. In many cases there is also a matter of minimizing power consumption to make sure that a tiny solar panel or a built-in battery is enough to power the device.

WHAT IS EMBEDL'S MODEL OPTIMIZATION SDK?

Trying to fit deep learning functionality to IoT devices is a challenge but can be required when there is a need to reduce bandwidth usage, protect privacy or make a decision on the device. The compute power in many IoT devices is provided by microcontrollers rather than SoCs and the available memory is usually measured in KB or MB rather than GB. 

Embedl Model Optimization SDK is used to shrink models to fit the available memory size so that they can be executed on IoT devices. Models can also be optimized to minimize inference time or power consumption on the device and can be applied to any type of target processor.

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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.

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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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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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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.

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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.

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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.

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