How does Tiny Grad solve this problem?

Tiny Grad solves the problem of quickly porting new ML accelerators by addressing the software shortcomings that many companies face when deploying their chips. According to George Hotz, the primary issue is the difficulty in writing a PyTorch port, which requires writing and optimizing around 250 kernels. This task is crucial because many companies' ML accelerators have great hardware but terrible software, making it challenging to achieve high performance 1.

George Hotz elaborates on the approach by highlighting that Tiny Grad's frontend is similar to PyTorch, making it familiar and easier for developers to transition their models. It also supports loading ONNX models, which facilitates the deployment of various machine learning models more cleanly and efficiently compared to PyTorch code 2. Furthermore, Tiny Grad simplifies matrix multiplications by reducing unnecessary memory operations through what Hotz terms "the power of laziness," optimizing operations before performing them 3.

In essence, Tiny Grad is designed to be a cleaner and more efficient alternative to existing frameworks by focusing on reducing the complexity and lines of code, specifically targeting neural networks and aiming to support a wide range of hardware 4.