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.
Blaize is among the many contenders in the AI chip space, sprung out of the renaissance brought on to hardware research and development by the proliferation of machine learning workloads. Although its architecture is interesting in its own right, the occasion to connect was something different. What is AI? Everything you need to know about Artificial Intelligence Blaize just launched its AI Studio solution, which it touts as the industry's first open and code-free software platform to span the complete edge AI operational workflow, dramatically reducing deployment time, complexity, and cost. Munagala and his co-founders, who were previously building graphics chips for Intel, started Blaize almost nine years ago, "pretty much from my spare bedroom" as per Munagala. They left because they wanted to build a new processor for emerging workloads -- a move ahead of its time, and ahead of Intel itself.
AI processing is changing the world order among CPU, GPU, and FPGA companies, with a host of AI processor startups joining the fray. The fight was once mostly in data centers, but they've all had to decamp to a new battlefield at the network edge. Driven by that premise, Blaize, an AI processor startup in El Dorado Hills, Calif., is heading straight to the edge with its just-announced AI hardware and software. The market forces sending AI inference to the edge are well understood. Privacy concerns, bandwidth issues (going back and forth between edge to cloud), latency and cost worries drive AI processing more and more edgeward.
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."
Start-up Blaize (formerly known as Thinci) has announced details of the first true Graph-Native silicon architecture and software built to process neural networks and enable AI applications. The Blaize Graph Streaming Processor (GSP) architecture enables concurrent execution of multiple neural networks and workflows on a single system. It also supports a range of heterogeneous compute-intensive workloads, says Blaize. According to Blaize, the computing architecture meets the demands and complexity of new computational workloads found in artificial intelligence (AI) applications in automotive, smart vision and enterprise computing segments. The Blaize GSP architecture and Blaize Picasso software development platform blends dynamic data flow methods and graph computing models with fully programmable proprietary SoCs.