REVOLUTIONIZING
CHIP Design

Quadric's Chimera General Purpose Neural Processing Unit (GPNPU)

Quadric has built a unified HW/SW architecture optimized for on-device artificial intelligence computing. Only the Quadric Chimera GPNPU delivers high ML inference performance and also runs complex C++ code without forcing the developer to artificially partition code between two or three different kinds of processors.

Quadric’s Chimera GPNPU is a licensable processor that scales from 1 to 16 TOPs and seamlessly intermixes scalar, vector and matrix code.

Design your Soc faster with Chimera GPNPU

Traditional Design

Quadric GPNPU Design

One architecture for ML inference plus pre-and-post processing simplifies SoC hardware design and software programming.

Three REASONS TO CHOOSE the chimera GPNPU

1
Handles matrix and vector operations and scalar (control) code in one execution pipeline. No need to artificially partition application code (C++ code, ML graph code) between different kinds of processors.
2
Executes diverse workloads with great efficiency, lower power and faster speed, all in a single processor.
3
Scales from 1 to 16 TOPs.
Find out more about the chimera GPNPU

Quadric Development studio

Quadric’s hosted SDK provides easy simulation and deployment of AI software.
Learn more about developer studio

Quadric Insights

Why GPNPU?

Quadric's GPNPU delivers benefits to both SoC developers and downstream software programmers, speeding both chip design and application development. Why Chip Designers Need a General Purpose Neural Processing Unit (GPNPU) The Rise of AI in […]

Read More
YOLO Detection – Accelerating Non Maximal Suppression

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 […]

Read More
Algorithm Stories: 1D FFT

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 […]

Read More
Why GPNPU?

Quadric's GPNPU delivers benefits to both SoC developers and downstream software programmers, speeding both chip design and application development. Why Chip Designers Need a General Purpose Neural Processing Unit (GPNPU) The Rise of AI in […]

Read More
YOLO Detection – Accelerating Non Maximal Suppression

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 […]

Read More
Explore more quadric blogs
© Copyright 2022  Quadric    All Rights Reserved     Privacy Policy
linkedin facebook pinterest youtube rss twitter instagram facebook-blank rss-blank linkedin-blank pinterest youtube twitter instagram