README.md

---
title: README
emoji: 🚀
colorFrom: green
colorTo: indigo
sdk: static
pinned: false
short_description: Empower AI inference
---

<img width="35%" src="./kalray_logo.png"></a></br>

Kalray enables AI innovators to build novel AI applications, maximizing your compute processing with MPPA Coolidge 2. Our
compute Acceleration Cards offer a very complementary architecture to GPUs, allowing for the processing a large number 
of different operations in parallel in an asynchronous way. Details can be found here:
* [Processor white paper](https://www.kalrayinc.com/resource/a6-mppa-coolidge-processor-white-paper/)
* [Computation cards](https://www.kalrayinc.com/products/dpu-processors/#turbocard4)
* [ML & computer vision](https://www.kalrayinc.com/solutions/#computer-vision)
* [SDK description]

You should find on this page several of the models that Kalray's SDK support. As part of ACE (AccessCore SDK), 
KaNN (Kalray Neural Network) is dedicated to optimize inference on the Kalray's processor (MPPA) on the following scheme:
1. Design and/or import your Neural Networks from ONNX or TensorFlow (PyTorch is supported using the ONNX bridge),
2. Build an intermediate represention of the NN in order to be executed on the MPPA,
3. Run and exploit predictions from the device

Find out on our github page the possibility to deploy and power your AI solutions over the Kalray's processor at: 
* [KANN-MODELS-ZOO](https://github.com/kalray/kann-models-zoo)
* [WIKI](https://github.com/kalray/kann-models-zoo/blob/main/WIKI.md)

Kalray 😎