Tensor to Tensor

Tensor-to-tensor operations are central to many modern machine learning applications, focusing on efficient manipulation and transformation of multi-dimensional data structures. Current research emphasizes optimizing these operations for speed and memory efficiency, particularly within deep learning frameworks, through techniques like tensorized algorithms (e.g., for evolutionary optimization), optimized memory allocation and scheduling (e.g., minimizing off-chip data access in DNN accelerators), and novel tensor decomposition methods (e.g., for faster completion in network latency estimation). These advancements are crucial for scaling up deep learning models and improving the performance of various applications, including robotic control, network analysis, and computer vision.