High Performance Embedded
We have a strong technical team, with long experience from many years of high performance embedded development.
High Performance Architecture
Designing high performance embedded systems has many challenges. In most cases a mix of several techniques has to be applied to reach the goal of a small, embedded system running at real-time and at the same time combining low-power with high performance. Hardware accelerators like FPGAs and embedded GPUs is often the solution, programmed to be compute engines for a specific task, off-loading the CPU. FPGAs can also form customized interfaces to sensors and other equipment, making them an attractive choice. Especially the fairly new SOC-FPGAs, combining one or several ARM-cores with an FPGA fabric, are very interesting system components as they more or less form a complete system, requiring very little additional components.
Synective’s specialities:
Automotive, ADAS
Data Acquisition
Algorithm development
Machine Learning and Neural Networking
Software acceleration
Image and signal processing
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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 actively researching and implementing machine learning with deep neural networks designed to fit on small edge FPGAs and other low power embedded systems.