Machine learning

Machine Learning empowers engineers to create applications that learn from data. The rise of modern graphics cards and the availability of more open data than ever has paved the way for unparalleled advances in Deep Learning used for anything from image classification, to text translation and text-to-speech.

Embedded Deep Learning

As the hardware capabilities have grown, so has the size of the neural networks. But in recent years there has been a shift in focus to make the networks as small as possible. This means that the power of Deep Learning is becoming available for mobile phones, IoT devices and other embedded systems. We at Synective are doing research to investigate how to best utilize modern machine learning using small and power efficient chips.


Deep Learning on FPGA

Deep Learning for embedded, edge systems is becoming an increasingly interesting technology. No need for internet connections – enabling decisions to be made right at the sensor which in turn reduces latency and downtime, both being critical for real time applications. This puts tough requirements on the compute system, in terms of number crunching as well as memory handling. Challenges that Synective actively works with to find the best possible solutions.

Object Detection Showcase

Synective has developed a demo system to showcase the power of embedded FPGA Deep Learning. It was able to classify hand-written digits using nothing but a tiny, low-end FPGA and a camera.

A Feed Forward Neural Network is a framework for basic machine learning classification or regression

A Convolutional Neural Network (CNN) is the go-to algorithm in Deep Learning for image related tasks.

Recurrent Neural Network (RNN) is a family of techniques that are often applied to time series.

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Our services

High Performance Embedded

Synective Labs provides high performance embedded solution to a wide range of customers, with a focus on computational and data intense problems.

FPGA/ASIC design

Synective Labs is a leading design house and consulting company within FPGA and ASIC design in the Nordic region.

IP block

The Synective CAN 2.0/CAN FD IP core implements a complete CAN controller for integration into FPGAs and ASICs.

Image and Signal processing

Synective Labs specializes in algorithm development for image and signal processing, with many years of experience from advanced camera, radar and sensor systems.

Server Based Acceleration

Synective Labs work with some of the world’s leading providers of hardware for server-based acceleration, in order to reach state of the art results.

Machine Learning

Synective Labs is researching the optimal implementation of deep neural networks on small edge FPGAs and other embedded systems.

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