This page provides answers to frequently asked questions about the Coral NPU platform.
Why does Google Research develop and offer this Kelvin platform?
The vision for the Coral NPU project was driven by market feedback and new technological opportunities. Our experience with Coral products highlighted a clear customer demand for a platform supporting ML frameworks beyond TensorFlow Lite. At the same time, the rise of Large Language Models (LLMs) presented a significant opportunity to bring powerful generative AI to edge devices efficiently.
Project Coral NPU leverages Google Research’s foundational work in open ML compilers (like IREE) and enhanced AI security, integrating these advancements into a unified, open-source platform. We believe an open approach is the best way to build a robust and innovative edge AI ecosystem, encouraging wide adoption and industry-wide collaboration.
Is Coral NPU meant to be the next generation Edge AI product to replace Coral?
It’s helpful to think of Coral NPU as an evolution of Google's edge AI strategy, rather than a direct replacement for Coral.
- Coral is a Google product suite of hardware modules built around our proprietary EdgeTPU chip.
- Coral NPU is an open-source NPU architecture that allows silicon partners and chip companies to build their own next-generation, energy-efficient AI accelerators.
The lessons we learned from Coral, especially the need for broader ML framework support and a more open approach.
What's the migration path for current Coral users to use Coral NPU-based hardware?
The migration path for Coral users is straightforward due to model compatibility. Coral NPU is an open platform that supports multiple ML frameworks, including TensorFlow, your existing models should run on new Coral NPU-based hardware either "as-is" or with minimal conversion.
This migration will be possible once our silicon partners have manufactured and released their new NPUs or SoCs. When that hardware is commercially available, you can run your existing models on it.
Are all the tools and designs in Coral NPU open source?
Yes, the Coral NPU platform is fully open source. This includes the NPU architecture, which is based on the open RISC-V standard, as well as the complete MLIR-based software toolchain. All code, documentation, and libraries are publicly available via our website and open-source repositories.
What operating systems does Coral NPU work with?
The Coral NPU runs bare-metal programs, targeting RV32IMFV (Zve32x). For developers working on the Coral NPU RTL, or wanting to run simulation, Debian Trixie is well tested, though other Linux flavors may work. Coral NPU does not impose any other restrictions on the operating system other cores in a host SoC may run.
Can my company use Coral NPU for commercial products and services?
Yes. Coral NPU is designed for broad commercial adoption. Any company or developer is free to build and sell commercial products or services based on the Coral NPU architecture and software toolchain.
Are license fees paid to Google if my product uses Coral NPU?
No, there are no license fees payable to Google for using the Project Coral NPU architecture or software. The platform is released under permissive open-source license Apache 2.0.
However, it is important to distinguish Google's open-source IP from third-party IP. If an SoC vendor chooses to integrate the Coral NPU with other proprietary, licensed components (such as a specific CPU core or memory fabric from another vendor), those third-party components would still be subject to their own respective licensing fees.
How does the NPU differ from the TPU, GPU, and DSP?
These are all types of processors that have different goals, architecture and optimization for different types of mathematical computations
TPU (Tensor Processing Unit): Google's proprietary ML accelerator for the Cloud. TPUs are designed to accelerate model training and high-performance inference in data centers.
GPU (Graphics Processing Unit): The industry standard for training complex AI models due to its massive parallelism and high memory bandwidth. GPUs are also widely used for high-throughput inference in servers and data centers.
DSP (Digital Signal Processor): A processor for real-time math on signals (like audio). It can run simple, low-latency inference tasks but is not designed for the heavy computation required for training.
NPU (Neural Processing Unit): The general term for a processor highly specialized for efficient AI inference on edge devices where power consumption is a key requirement.
What are the industry resources that I can leverage to help me integrate Coral NPU?
The primary resource is our growing open-source ecosystem, which includes hardware partners, chip design services, and the software toolchain. We are actively collaborating with silicon partners to integrate the Coral NPU architecture into their commercial chips. For example, our launch partner Synaptics is releasing its Torq NPU, an SoC solution ideal for next-generation IoT and wearable devices.
For a complete list of available hardware, design partners, and software resources, please visit our Ecosystem & Partners page.
Are there business services available that can help me turn my Coral NPU-integrated design into chip design?
Yes. As part of our ecosystem, we are partnering with leading chip design service companies. For example, Verisilicon, a leader in RISC-V chip design, provides expert services to help vendors integrate the Coral NPU into their custom products. For more details, please refer to our Partners page.
Is it possible to design an AI/ML accelerator chip targeting the IoT device market?
Yes, absolutely. The Coral NPU architecture is scalable to meet the diverse needs of the IoT market. While initial implementations target performance in the hundreds of gops(giga-operations per second), the architecture allows partners to implement more powerful designs targeting the multi- tops(trillions-of-operations per second) range for more demanding use cases.