added compat note for pascal gpus and cuda12+jax

Author: Alexander Ororbia

README.md

@@ -45,6 +45,25 @@ Matplotlib (>=3.8.0) and imageio (>=2.31.5) and both plotting and density estima
 tools (routines within ``ngclearn.utils.density``) will require Scikit-learn (>=0.24.2).
 Many of the tutorials will require Matplotlib (>=3.8.0), imageio (>=2.31.5), and Scikit-learn (>=0.24.2).
 
+<i>Note</i>: If you are working with Cuda 12 and want to use jax/jaxlib versions > 0.4.28, you might need to 
+check that you are working with the right version of Cudnn (e.g., `nvidia-cudnn-cu12==9.10.2.21`) to ensure 
+that all of ngc-learn's internal supported tools, like in-built convolution/deconvolution, compile 
+correctly onto the GPU (if using an architecture based on Pascal GPUs, i.e., Compute Capability 6.1, 
+combined with NVIDIA Driver 580+). 
+
+**Important Note for Legacy GPU Users (Pascal Architecture)**
+> If you are running JAX (`> 0.4.28`) on **CUDA 12** using an older **Pascal-generation GPU** (Compute Capability 6.1, 
+> e.g., GTX 1080/1080Ti, Titan X) combined with **NVIDIA Driver 580+**, you might encounter compilation crashes during 
+> convolution/deconvolution operations (such as `unknown cudnn status: 5003`).
+> 
+> Newer versions of `nvidia-cudnn-cu12` have dropped critical hardware support for these legacy architectures. To 
+> fix this and ensure `ngclearn` compiles correctly on your GPU, you will need to explicitly "pin" your cuDNN 
+> library version using this command (after installing Cuda-12 JAX):
+>
+> ```bash
+> pip install --force-reinstall "nvidia-cudnn-cu12==9.10.2.21"
+> ```
+
 ### User Installation
 
 <i>Setup</i>: The easiest way to install ngc-learn is through <code>pip</code>: