GEMESYS funding news – Germany-based GEMESYS has Raised €8.6Million in a Pre-Seed Funding
Nov 20, 2024 | By Kailee Rainse
AI hardware startup GEMESYS has secured €8.6M in a pre-seed funding round to strengthen its position as a European leader in advanced AI chip technology.
SUMMARY
- AI hardware startup GEMESYS has secured €8.6M in a pre-seed funding round to strengthen its position as a European leader in advanced AI chip technology.
- GEMESYS has developed an analog chip inspired by how the human brain processes information.
Investor of the round
The round was led by Amadeus APEX Technology Fund and Atlantic Labs, with support from NRW.BANK, Sony Innovation Fund, and Plug and Play Tech Center.
Funding utilisation
The funding will help it yo speed up research, enhance its chip design, and grow its team.
While AI hardware is largely dominated by Asia and the US, it aims to establish Europe as a key player in this field, driving innovation and technological independence.
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Neural networks imitate the human brain but are far less efficient, using a lot of energy for training and operation. This makes it challenging to develop true AI for mobile devices.
It's aim to improve on-device AI by using its unique architecture and memristor technology. This allows edge devices to learn and process data locally, unlocking new possibilities for various applications.
Dr. Dennis Michaelis, CEO of GEMESYS commented, “At GEMESYS, our mission is to bring learning capabilities to every device through technology developed in Europe. Building on this foundation, we’re pioneering new applications that empower edge devices like never before positioning Europe as a leader in distributed intelligence and setting a new standard for industry innovation.”
Ion Hauer of APEX Ventures expressed his support, saying, “We’re excited to see GEMESYS redefine what’s possible in Edge AI. Its technology can transform countless industries and push the boundaries of on-device inference and training.”
About GEMESYS
GEMESYS has developed an analog chip inspired by how the human brain processes information. This chip trains neural networks with 20,000 times better energy efficiency than current technology, offering a groundbreaking solution for AI hardware.