Funding

Synthara funding news – Semiconductor Innovator Synthara Secures €10.5 Million in Funding

Jun 5, 2024 | By Team SR

Zurich-based Semiconductor Innovator Synthara secures €10.5 million in funding through investments and European and Swiss grants to bring in-memory computing to the mass market.

Zurich-based Semiconductor Innovator Synthara secures €10.5 million in funding through investments and European and Swiss grants to bring in-memory computing to the mass market.

SUMMARY

  • Zurich-based Semiconductor Innovator Synthara secures €10.5 million in funding.
  • Synthara was established in 2019 as a spin-off from the University of Zurich and the Institute of Neuroinformatics at ETH Zurich.

Lead investors in the round include High-Tech Gründerfonds, DeepIE, Excellis, ZKB, OTB Ventures, Hermann Hauser's Onsight Ventures, Deep Tech Labs, and inaugural investor and early adopter Sandeep Raju. Hermann Hauser became an advisor for the corporation as well.

With this support, Synthara will be able to present ComputeRAM, a ground-breaking patent-pending device, to embedded computing applications.

Robotics, sensors, and wearables are just a few of the AI-rich embedded applications that are proliferating and are worth over $200 billion, with a compound annual growth rate of over 15%. The problem is that these uses require too much complexity for traditional embedded circuits. Costs and time to market are rising as embedded device manufacturers sift through an ever-more-complex ecosystem of hardware and software offerings.

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The solution to these issues requires a paradigm shift in computer technology. ComputeRAM makes micro-controllers 100 times more efficient in terms of latency and energy consumption by adding computational capabilities to traditional on-chip memories.

By addressing a wide range of AI-rich applications, these versatile and high-performance micro-controller circuits can reduce costs and time to market, opening up new prospects for device and chip makers.

Manu V Nair, Co-Founder and CEO of Synthara, said: “With ComputeRAM, we are looking to enable a new breed of feature-rich products that are only limited by the creativity of the developers. This founding round empowers us to work closely with our early adopter community as they develop game-changing chips and devices.”

Patrick Tucci, Principal at Vsquared Ventures, commented: “We are about to witness a paradigm shift in computing, with in-memory-computing leading the charge. The bottleneck of data movement is especially noticeable in edge applications. Here, smart devices need to run complex, concurrent algorithms. This problem is compounded by space limitations and low power requirements. To continue advancing and fostering innovation, we require high-performance, drop-in solutions such as Synthara’s ComputeRAM.”

Karol Szubstarski, Partner at OTB Ventures, added: “The emerging in-memory computing (IMC) paradigm is set to transform the industry, enabling it to bypass the data transfer bottlenecks in conventional chip architectures that affect energy consumption, performance & latency. Synthara enables easy integration of the underlying principles of IMC technology into existing chip design without any disruptive alterations to the architecture.”

Apart from seeking venture money, Synthara is engaged in other noteworthy European initiatives that focus on advanced computing and AI applications of the future generation (Rebecca, Centric, and Tristan).

Significant financial support has also been obtained by the business from Innosuisse and SERI, including a recent CHF 2.5 million Swiss Accelerator award to develop cutting-edge AI chips for the future. The 20-person team works out of Zurich. Prior investors include Excellis, ZKB, HTGF, and DeepIE.

About Synthara

Synthara was established in 2019 as a spin-off from the University of Zurich and the Institute of Neuroinformatics at ETH Zurich. Drs. Manu V. Nair and Alessandro Aimar's PhD research served as the foundation for Synthara.

The processing systems of the future for wearables, IoT, communication, and XR have strict performance and energy-efficiency standards. Integration of in-memory computing capabilities is a must to achieve these objectives. But it can be slow and complicated.

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