Neural Accelerator

Neural accelerators are specialized hardware designed to significantly speed up and improve the energy efficiency of deep neural network computations. Current research focuses on optimizing these accelerators for various neural network architectures, including attention mechanisms and spiking neural networks, and addressing challenges like efficient memory access and handling variations in emerging memory technologies through techniques such as selective write-verify and importance sampling for fault tolerance. These advancements are crucial for deploying deep learning models on resource-constrained edge devices and improving the robustness and performance of AI systems across diverse applications.