Israel-based start-up Speedata wants to become a competitor against the big players in the semiconductor sector. Recently, the venture developed an analytics processing unit (APU) designed to accelerate big data analytic and AI workloads, apart from raising a USD 44 million Series B funding round, bringing its total capital raised to USD 114 million.
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) developed by the existing semiconductor sector players, which were initially designed for graphics and later modified for AI and data-related tasks. In today’s episode of the “Start-up of the Week,” International Finance will talk about Speedata and its game-changing APU.
Transforming The Data Centre Landscape
Speedata claims its purpose-built APU to be the first of its kind, specifically designed for accelerating database analytics and AI workloads. The solution also offers unparalleled scalability and efficiency, seamlessly integrating hardware and software into a powerful, unified platform.
“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,” said Adi Gelvan, CEO of Speedata, in an interview with TechCrunch.
The start-up was founded in 2019 by six founders (including Gelvan), some of whom were the first researchers to develop Multi-Threaded Coarse-Grained Reconfigurable Architecture (CGRA) technology.
The founders collaborated with ASIC design experts to address a fundamental problem related to the data analytics being performed by general-purpose processors. If the workloads grew too complex, they could need to tap into hundreds of servers. Gelvan and his colleagues believed that they could develop a single dedicated processor to accomplish the task faster using less energy.
Talking about Speedata’s APU, the product currently targets Apache Spark workloads (an open-source, distributed processing system used for big data workloads). As per Gelvan, in the long run, the unit will support every major data analytics platform.
“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 stated.
The start-up reportedly has several large companies testing its APU. Speedata also 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-specialised processing unit, resulting in a 280 times speed improvement.
The start-up also has achieved several milestones since its last fundraising, including finalising 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 we’re ready to scale our go-to-market operations,” Gelvan said.
Meet The Game-Changing APU
The Speedata APU achieves its breakthrough throughput by mapping the required processing into its internal hardware pipeline. The Speedata Dash software automatically configures a data flow in silicon, where row processing is broken into hundreds of steps, each efficiently flowing to the next one at every hardware clock. Therefore, at any given time, hundreds of rows are at different stages of processing in the hardware, in parallel, resulting in a processing throughput of over a billion rows per second.
Speedata APU also ensures accelerated Parquet processing in hardware. Parquet is the leading file format for analytics. Speedata’s APU efficiently processes Parquet files as part of its hardware pipeline, from decompressing and decoding columns through columnar filters and projections to rows assembly and flattening of nested data (EXPLODE). And then comes Speedata APU’s seamless integration with Apache Spark.
“Speedata’s Dash software transparently plugs into the Spark Catalyst optimiser to automatically identify and offload compute-intensive work to the APU, delivering dramatic acceleration for Apache Spark 3.x workloads on Kubernetes, YARN and standalone cluster managers,” the start-up stated.
Speedata C200 is the industry’s first analytics accelerator card, housing the start-up’s APU chip.
“It is optimised for high-bandwidth data processing and delivers dramatic acceleration for Apache Spark workloads. With its PCIe connectivity, it is engineered for seamless integration into standard server configurations,” Speedata noted.
With flexible server deployment options, Speedata makes it easy to bring the power of the APU to data centres, with customers having the freedom to choose between two flexible procurement models. One of them is buying pre-configured from the start-up, under which the latter provides a fully integrated 2U server with two C200 analytics accelerator cards, pre-configured and ready to accelerate Apache Spark workloads out of the box.
Under the second option, Speedata offers data centre servers equipped with C200 analytics accelerator cards through trusted OEM providers such as HPE. These servers can be customised in terms of model and configuration to meet the client’s needs.
To ensure seamless integration with Apache Spark, Speedata’s Dash software transparently plugs into the Spark Catalyst optimiser to automatically identify and offload compute-intensive work to the APU. It automatically delegates to the CPU the handling of SQL (Structured Query Language) elements such as UDFs (User-Defined Function) that cannot be accelerated, while still handling the rest of the query, guaranteeing great performance.
The Workload Analyser is a standalone tool for analysing Apache Spark event log files and identifying the projected acceleration that Speedata delivers for its clients’ workloads. The latter can quickly learn which queries will benefit the most, whether a faster network will have a big impact on the business’ APU environment or not, or see a detailed per-stage analysis of the benefits or limits.