Speedata, a Tel Aviv-based startup developing an analytics processing unit (APU) designed to accelerate big data analytic and AI workloads, has raised a $44M Series B funding round, bringing its total capital raised to $114M.
The Series B round was led by its existing investors, including Walden Catalyst Ventures, 83North, Koch Disruptive Technologies, Pitango First, and Viola Ventures, as well as strategic investors, including Lip-Bu Tan, CEO of Intel and Managing Partner at Walden Catalyst Ventures, and Eyal Waldman, Co-Founder and former CEO of Mellanox Technologies.
The APU architecture focuses on addressing the specific bottlenecks of analytics at the computing level, unlike graphics processing units (GPUs), which were initially designed for graphics and later modified for AI and data-related tasks, according to the startup.
“For decades, data analytics have relied on standard processing units, and more recently, companies like Nvidia have invested in pushing GPUs for analytics workloads,” Adi Gelvan, CEO of Speedata, said in an interview with TechCrunch. “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.”
Speedata was founded in 2019 by six founders, some of whom were the first researchers to develop Coarse-Grained Reconfigurable Architecture (CGRA) technology. The founders collaborated with ASIC design experts to address a fundamental problem: data analytics were being performed by general-purpose processors. If the workloads grew too complex, they could need to tap into hundreds of servers. The founders believed that they could develop a single dedicated processor to accomplish the task faster using less energy.
“We saw this as an opportunity to put our decades of research in silicon into transforming how the industry processes data,” Gelvan said.
Its APU currently targets Apache Spark workloads, but its roadmap includes supporting every major data analytics platform, according to the company CEO.
“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 says it has a number of large companies testing its APU, though it declined to name them. The official product launch is set for the Databricks’ Data & AI Summit in the second week of June. Gelvan said that they will publicly showcase its APU for the first time at the event.
Speedata claims a specific case where its APU completed a pharmaceutical workload in 19 minutes, which was significantly faster than the 90 hours it took when using a non-specialized processing unit, resulting in a 280x speed improvement.
The startup said it has achieved several milestones since its last fundraising, including finalizing the design and manufacturing of its first APU in late 2024.
“We’ve moved from concept to testing on a field-programmable gate array (FPGA), and now we are proud to say we have working hardware that we are currently launching. We already have a growing pipeline of enterprise customers eagerly waiting for this technology and were ready to scale our go-to-market operations,” Gelvan, said.
