Update README.md

README.md

@@ -10,13 +10,22 @@ 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 on TC4.
-Kalray’s TURBOCARD4 (TC4) Compute Acceleration Cards enable customers to accelerate AI workloads. Our compute Acceleration Cards 
-offer a very complementary architecture to GPUs, allowing for the processing of a large number of different operations in 
-parallel in an asynchronous way. TC4 is well-suited for pre-processing data that is later used by GPUs or in the context of 
-complex intelligent systems running many different algorithms in parallel. Details can be found here:
-[link](https://www.kalrayinc.com/products/dpu-processors/#turbocard4)
+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](https://www.kalrayinc.com/products/accesscorer-embedded-ace/)
 
-You should find on this page several of the models that Kalray's SDK support, called Kalray Neural Networks (KaNN). Find out
-on our github page the possibility to deploy and power your AI solutions over the Kalray's processor at 
-https://github.com/kalray/kann-models-zoo
+You should find on this page several of the models that Kalray's SDK support. A part of the ACE (AccessCore Embedded), 
+called 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: 
+* https://github.com/kalray/kann-models-zoo
+* [WIKI](https://github.com/qmller/kann-models-zoo/blob/main/WIKI.md)
+
+Quentin, for Kalray.