Sachin Muradi enhanced the tensorflow/tensorflow repository’s CPU backend by developing asynchronous execution capabilities for OneDnnFusionThunk, introducing a threadpool to enable concurrent task processing and updating the Invoke method to return AsyncValueRef for improved event management. Working in C++ with a focus on asynchronous programming and performance optimization, Sachin also addressed a critical bug in OneDnnFusion by ensuring unique logical tensor IDs for parameters, which corrected matmul fusion accuracy and runtime efficiency. The work demonstrated a deep understanding of DNN fusion internals and contributed to more stable, performant model execution for users relying on OneDnn-enabled fusion paths.