The recent advancements in specialized hardware for deep neural networks (DNNs) have seen a shift towards optimizing task mapping and memory bandwidth utilization. Researchers are increasingly focusing on dynamic task allocation methods that leverage real-time network congestion data to improve performance, moving away from static mapping strategies. Additionally, there is a growing emphasis on developing pipelining strategies that maximize off-chip memory bandwidth utilization, particularly in processing-in-memory (PIM) accelerators, to handle the increasing size of DNN models. These developments indicate a trend towards more adaptive and efficient hardware solutions that can better accommodate the evolving demands of DNNs. Notably, innovative neural architecture search techniques are also being employed to enhance the routability prediction in electronic design automation, demonstrating a convergence of machine learning with hardware optimization strategies.