Deep Learning Acceleration

Deep learning acceleration focuses on developing hardware and algorithmic techniques to significantly speed up and reduce the energy consumption of deep neural network (DNN) computations. Current research emphasizes novel architectures like processing-in-memory (PIM) systems, optical computing, and event-driven designs, alongside algorithmic optimizations such as quantization, pruning, and sparsity exploitation across various DNN models (e.g., CNNs, GNNs). These advancements are crucial for deploying DNNs on resource-constrained devices like embedded systems and edge computing platforms, enabling real-time applications in diverse fields while addressing sustainability concerns.