TECHNOLOGY

At EMBRYA, we create innovative AI/ML accelerators delivering increased performance and flexibility and reduced SWaP for intelligent applications on embedded systems.

Generic low-footprint Chip IPs resizable according to customer's needs.

On the fly Zero-Code ANN reconfiguration (inference).

No limit on available memory.

Integrability on : FPGA, CPU, GPU, ASICS, SoC/NoC, Federated Learning Architectures

Genericity

Our AI solutions are designed to be usable within multiple applications, domains and problems.

This versatility is crucial in a world where AI is increasingly used across diverse fields, from space to automotive applications, and from IoT to Edge Computing devices.

Our generic solutions can integrate all the features of several Artificial Neural Networks within one component and allow the selection of these features by configuration for a selected set of applications.

It allows the sharing of resources and expertise, and reduces the time and investment needed to develop solutions for individual problems.

Unlimited memory

Based on an innovative memory handling, our technology can use unlimited external memories.

Therefore, your AI applications within embedded systems can be integrated with more complex and powerful algorithms, and with more sophisticated and accurate models, leading to improved performance in tasks like image and speech recognition, real-time analytics, and complex decision- making processes.

It also brings greater flexibility in developing and scaling your AI applications.

Your designers and developers can implement more advanced features without worrying about memory constraints.

Online learning

For some applications, online learning allows systems to adapt and learn from real-time data, improving their performance over time.

It allows the systems to quickly adapt to new patterns or anomalies, enhancing their overall reliability and effectiveness.

This feature is barely supported on embedded devices due to the complexity of the underlying algorithms and models.

EMBRYA’s technology allows online learning.

Minimized SWaP

Minimizing size and power unlocks possibilities for embedding AI technology in new, previously impractical environments.

This includes extreme conditions like space exploration, deep-sea monitoring, and embedded medical devices.

Overall, in these sectors, the drive towards minimizing size and power consumption is fueled by the need for portability, long battery life, user comfort, and the ability to operate in challenging or remote environments.

This megatrend is a key factor in the ongoing innovation and expansion of capabilities in the field of embedded AI technology.

Our Technology

At EMBRYA, we create innovative AI/ML accelerators delivering increased performance and flexibility and reduced SWaP for intelligent applications on embedded systems.

Generic low-footprint Chip IPs resizable according to customer’s needs

On the fly Zero-Code ANN reconfiguration (inference)

No limit on available memory

Integrability on: FPGA, CPU, GPU, ASICS, SoC/NoC, Federated Learning Architectures…

Minimized SWaP

Genericity

Our AI solutions are designed to be usable within multiple applications, domains and problems.

This versatility is crucial in a world where AI is increasingly used across diverse fields, from space to automotive applications, and from IoT to Edge Computing devices.

Our generic solutions can integrate all the features of several Artificial Neural Networks within one component and allow the selection of these features by configuration for a selected set of applications.

It allows the sharing of resources and expertise, and reduces the time and investment needed to develop solutions for individual problems.

Unlimited memory

Based on an innovative memory handling, our technology can use unlimited external memories.

Therefore, your AI applications within embedded systems can be integrated with more complex and powerful algorithms, and with more sophisticated and accurate models, leading to improved performance in tasks like image and speech recognition, real-time analytics, and complex decision- making processes.

It also brings greater flexibility in developing and scaling your AI applications.

Your designers and developers can implement more advanced features without worrying about memory constraints.

Online learning

For some applications, online learning allows systems to adapt and learn from real-time data, improving their performance over time.

It allows the systems to quickly adapt to new patterns or anomalies, enhancing their overall reliability and effectiveness.

This feature is barely supported on embedded devices due to the complexity of the underlying algorithms and models.

EMBRYA’s technology allows online learning.

Minimized SWaP

Minimizing size and power unlocks possibilities for embedding AI technology in new, previously impractical environments.

This includes extreme conditions like space exploration, deep-sea monitoring, and embedded medical devices.

Overall, in these sectors, the drive towards minimizing size and power consumption is fueled by the need for portability, long battery life, user comfort, and the ability to operate in challenging or remote environments.

This megatrend is a key factor in the ongoing innovation and expansion of capabilities in the field of embedded AI technology.