RaiderChip raises 1 Million Euros in seed capital to market its innovative generative AI accelerator: the GenAI v1.
The AI-focused hardware design company secures funding from a business angel to further develop acceleration solutions for its clients.
Spain -- May 20, 2024 -- RaiderChip, a hardware design company, announced today the closing of its seed capital round. The fully subscribed round raised one million Euros in new capital to drive the next phase of the company’s growth. The funds will be primarily allocated to market its innovative AI acceleration engine.
RaiderChip is developing IP core hardware solutions aimed at accelerating Artificial Intelligence on FPGA platforms. Its extraordinary efficiency allows it to offer acceleration entirely locally, without subscriptions, and on low-cost devices. Designed to operate both fully autonomously and embedded, the GenAI v1 supports various language models: from the Microsoft Phi-2 Small Language Model, for smaller and simpler solutions, to the Meta Llama-2 and Llama-3 Large Language Models for broader and more complex applications.
Founded as a spin-off from VISENGI, the RaiderChip team has extensive experience in low-level hardware design. Among their achievements is the development of the fastest H.264 video encoder on the market, unbeaten to date with its 8K performance on low-cost devices. Today, their goal is to bring Generative AI acceleration to Edge markets. In the words of the CTO, Victor Lopez: “Our goal is harnessing our two decades of experience in semiconductor design to achieve the maximum efficiency also in the field of AI, where the demand for computing resources makes the optimal resource usage absolutely crucial.”
With the new funds, RaiderChip will continue to deepen the pursuit of more efficient and versatile accelerators capable of meeting the growing market demand for local acceleration solutions in a field as open to innovation as Artificial Intelligence.
Related Semiconductor IP
- AI accelerator
- RISC-V-Based, Open Source AI Accelerator for the Edge
- Lowest Power and Cost End Point AI Accelerator
- Performance AI Accelerator for Edge Computing
- Performance Efficiency AI Accelerator
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