In July of 1887, Carl Benz held the first public outing for his “vehicle powered by a gas engine” – the first automobile ever invented. Nine short months after the first car was publicly displayed, […]

On Chimera QC Ultra it runs 28x faster than DSP+NPU ConvNext is one of today’s leading new ML networks. Frankly, it just doesn’t run on most NPUs. So if your AI/ML solution relies on an […]

Burlingame, California USA, and Premstaetten, Austria, January 5, 2023 -- Quadric, an innovator in machine learning processor IP for on-device AI, and ams OSRAM (SIX: AMS), a global leader in optical solutions, today announced a […]

Additional $10M brings Series B total to $31M Burlingame, CA – December 15, 2022 – Quadric, the general purpose neural processing unit (GPNPU) processor IP market leader, announced today an extension and completion of its Series […]

Experienced Semiconductor IP Engineering Leader Adds Deep Processor Design Expertise to Fast Growing Team Burlingame, CA – October 11, 2022 – Quadric, the GPNPU intellectual property (IP) licensor, today announced that Dhanendra Jani, a former […]

The ResNet family of machine learning algorithms, introduced to the AI world in 2015, pushed AI forward in new ways. However, today’s leading edge classifier networks – such as the Vision Transformer (ViT) family -  […]

In this blog post, quadric explores the acceleration of the Non-Maximal Suppression (NMS) algorithm used by object detection neural networks such as Tiny Yolo V3. We dive into the challenges of accelerating NMS, and why […]

The first in a series of Algorithm Stories. In this blog post, the first in a series, we would like to reinforce the idea that our architecture is code-driven. On our architecture overview page, we […]

Collaboration Will Leverage High-Performance Quadric GPNPU Architecture to Deliver On-Device AI Solutions BURLINGAME, Calif., and OSAKA, Japan, May 17, 2022 – Quadric, an innovator in high-performance edge processing platforms for on-device AI, and MegaChips, a leading ASIC […]

We want to drive home the point that along with Neural Network acceleration, we also accelerate data parallelism for classical algorithms, as well. We selected the Fourier Transform because of its historical and continued importance […]