Quadric and MegaChips Corp. have formed a strategic partnership to deliver ASIC and SoC solutions built on Quadric’s groundbreaking edge AI processor architecture.

MegaChips announced an equity stake in Quadric in January 2022 and is also a major investor in a $21 million Series B funding round announced in March through their MegaChips LSI USA Corp. subsidiary. The round aims to help Quadric release the next version of its processor architecture, improve the performance and breadth of the Quadric software development kit (SDK), and roll out IP products to be integrated in MegaChips’ ASICs and SoCs.

Quadric offers the unique ability to handle both neural backbones and classical dynamic data-parallel algorithms in a unified architecture, bringing advanced on-device AI capabilities to edge-based applications. The Quadric architecture is the industry’s first General Purpose Neural Processing Unit (GPNPU). Quadric’s architecture delivers high machine learning (ML) inference performance, but unlike other neural network accelerators which support a limited number of machine learning graph operators, the Quadric solution also has general purpose control and signal processing capability—blending the best attributes of NPU accelerators with DSPs. Quadric GPNPUs can run both neural net graphs and C++ code for signal pre-processing and post-processing.

While many other edge processing solutions combine high-power CPU clusters or exotic DSPs with application-specific network processing units (NPUs), Quadric’s GPNPU architecture provides the flexibility to accelerate the entire application pipeline without the need for a companion processor paired with the NPU.

“Our continued investment in edge AI leaders like Quadric reflects our commitment to supporting disruptive AI-based on-device computing solutions,” said Douglas Fairbairn, Director, Business Development at MegaChips LSI USA. “Quadric’s stellar team, market traction with key customers and innovative edge AI solution contributed to our decision to invest in Quadric. We are proud to work with Quadric to bring its end-to-end IP products to our mutual customers in the form of customized silicon.”

MegaChips’ and Quadric’s collaboration addresses a growing industry trend in which cloud-based AI, machine learning (ML) and inference are migrating to the network edge. This migration requires powerful on-device AI processing capabilities to handle the massive amounts of data generated by billions of IoT devices and edge-based applications. Target applications for MegaChips’ ASICs and SoCs based on Quadric’s IP include autonomous driving (ADAS/LiDAR), industrial robotics, factory automation, automated guided vehicles (AGV) and advanced medical equipment.

“It’s an exciting time to partner with a semiconductor innovator like MegaChips to bring accelerated computing to edge devices,” said Veerbhan Kheterpal, co-founder and CEO of Quadric. “While the AI market is saturated with rigid accelerators that only address a portion of the machine learning compute challenge, our processing platform fills the void with a fully programmable, multi-kernel processing architecture. We’ve reimagined a general-purpose processor architecture and built it from scratch to give developers what they want: a versatile, high-performance processing platform that can support any type of AI and ML algorithm with an open ecosystem.”

As technology partners, MegaChips and Quadric are dedicated to bringing a broad range of solutions to customers building products that require edge AI capabilities. MegaChips has more than 30 years of experience delivering custom ASIC products and services to a wide range of customers worldwide. With strong internal design skills in AI, analog, image processing and other key technologies, MegaChips transforms design specifications into cost-effective, high-quality ASICs and SoCs. As a full-service semiconductor supplier with extensive engineering and manufacturing resources, MegaChips offers end-to-end foundry, packaging, testing and qualification capabilities to turn great design ideas into high-volume production silicon.

By: DocMemory
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