Building a PyTorch Backend for a Custom Accelerator
Custom AI hardware creates value when repeated model operations can be moved into workload-specific datapaths for better latency, power, and integration. This post shows a small PyTorch-facing prototype that connects ordinary tensor code to that hardware path.