El DORADO HILLS, CA -- November 12, 2019 -- BlaizeTM today emerged from stealth and unveiled a groundbreaking next-generation computing architecture that precisely meets the demands and complexity of new computational workloads found in artificial intelligence (AI) applications. Driven by advances in energy efficiency, flexibility, and usability, Blaize products enable a range of existing and new AI use cases in the automotive, smart vision, and enterprise computing segments, where the company is engaged with early access customers. These AI systems markets are projected to grow rapidly* as the disrupting influence of AI transforms entire industries and AI functionality becomes a "must-have" requirement for new products. "Blaize was founded on a vision of a better way to compute the workloads of the future by rethinking the fundamental software and processor architecture," says Dinakar Munagala, Co-founder and CEO, Blaize. "We see demand from customers across markets for new computing solutions that address the immediate unmet needs for technology built for the emerging age of AI, and solutions that overcome the limitations of power, complexity and cost of legacy computing."

There's booming demand for silicon custom-designed to accelerate AI workloads, as the gobs of cash raised by startups like Hailo Technologies, Graphcore, and Untether AI demonstrates. The fierce competition isn't deterring Blaize (formerly Thinci), which hopes to stand out from the crowd with a novel graph streaming architecture. The nine-year-old startup's claimed system-on-chip performance is impressive, to be fair, which is likely why it's raised nearly $100 million from investors including automotive component maker Denso. Blaize emerged from stealth today with $87 million raised over several venture rounds from strategic and venture backers Denso, Daimler, SPARX Group, Magna, Samsung Catalyst Fund, Temasek, GGV Capital, SGInnovate, and Magna; the second-to-last round closed in September 2018 and totaled $65 million. The company initially focused on what it called vision processors -- chips to speed up vision, radar, and sensor fusion tasks -- before expanding to encompass datacenters, edge infrastructure devices, and enterprise client devices.