DNN Acceleration

DNN acceleration focuses on optimizing deep neural network performance and energy efficiency, primarily addressing the computational demands of increasingly complex models. Current research emphasizes techniques like model pruning (including novel unaligned block-wise methods), hardware-software co-design for specialized architectures (e.g., RISC-V based SoCs and optical computing), and algorithmic innovations such as event-driven processing. These advancements are crucial for deploying DNNs on resource-constrained devices (mobile, IoT) and high-performance computing platforms, impacting diverse fields from mobile AI to large-scale data centers.