Speedata, an innovative chip startup based in Tel Aviv, has successfully raised $44 million in a Series B funding round, bringing its total capital to $114 million. This round was led by existing investors, including Walden Catalyst Ventures, 83North, Koch Disruptive Technologies, Pitango First, and Viola Ventures. Notably, strategic investors such as Lip-Bu Tan, CEO of Intel and Managing Partner at Walden Catalyst Ventures, and Eyal Waldman, Co-Founder and former CEO of Mellanox Technologies, also participated.
Founded in 2019, Speedata is pioneering the development of an Analytics Processing Unit (APU) designed to accelerate big data analytics and artificial intelligence workloads. Unlike traditional graphics processing units (GPUs) that were initially tailored for graphics and later adapted for AI and data tasks, Speedata’s APU is purpose-built from the ground up for data processing. This specialized focus allows a single APU to replace multiple server racks, delivering significantly enhanced performance and energy efficiency.
The company’s APU architecture addresses specific bottlenecks in data analytics at the computing level. Traditional processors, including GPUs, often struggle with the demands of complex data analytics tasks, leading to increased hardware requirements and energy consumption. Speedata’s solution offers a more efficient alternative by providing a dedicated processor optimized for these workloads.
Adi Gelvan, CEO of Speedata, emphasized the transformative potential of their technology:
For decades, data analytics have relied on standard processing units, and more recently, companies like Nvidia have invested in pushing GPUs for analytics workloads. But these are either general-purpose processors or processors designed for other workloads, not chips built from the ground up for data analytics. Our APU is purpose-built for data processing, and a single APU can replace racks of servers, delivering dramatically better performance.
The inception of Speedata was driven by a team of six founders, some of whom were pioneers in developing Coarse-Grained Reconfigurable Architecture (CGRA) technology. Collaborating with experts in ASIC design, they identified a critical issue: data analytics were being performed by general-purpose processors, which, when faced with complex workloads, required extensive server resources. The founders envisioned a dedicated processor capable of executing these tasks more rapidly and with reduced energy consumption.
We saw this as an opportunity to put our decades of research in silicon into transforming how the industry processes data, Gelvan stated.
Currently, Speedata’s APU targets Apache Spark workloads, a widely used open-source analytics engine for big data processing. However, the company’s roadmap includes expanding support to encompass every major data analytics platform. This strategic direction aims to position the APU as the standard processor for data processing, analogous to how GPUs have become the default for AI training.
We aim at becoming the standard processor for data processing—just as GPUs became the default for AI training, we want APUs to be the default for data analytics across every database and analytics platform, Gelvan told TechCrunch.
The startup reports that several large companies are currently testing its APU, though specific names have not been disclosed. The official product launch is scheduled for the Databricks’ Data & AI Summit in the second week of June, where Speedata will publicly showcase its groundbreaking technology.
The significance of Speedata’s innovation lies in its potential to address the exponential growth of data and the corresponding need for efficient processing solutions. Traditional processors are increasingly inadequate for handling the vast volumes of data generated daily. By offering a dedicated processor tailored for data analytics, Speedata aims to provide a solution that not only enhances performance but also reduces operational costs and energy consumption.
The participation of prominent investors underscores the confidence in Speedata’s technology and its potential impact on the industry. Lip-Bu Tan and Eyal Waldman bring a wealth of experience and strategic insight, which will be invaluable as Speedata moves towards commercialization and broader market adoption.
In summary, Speedata’s recent funding and the development of its APU mark a significant advancement in the field of data analytics processing. By providing a processor specifically designed for these workloads, Speedata is poised to revolutionize how data is processed, offering a more efficient and scalable solution for the challenges posed by big data.
