Deep Learning Accelerator

Deep learning accelerators are specialized hardware designed to efficiently execute deep neural network computations, primarily aiming to improve speed and reduce energy consumption for various applications. Current research emphasizes optimizing accelerator architectures (e.g., systolic arrays, FPGAs) through techniques like automated performance modeling, energy-efficient configuration strategies, and innovative data representation methods to accelerate training and inference. These advancements are crucial for deploying deep learning models on resource-constrained devices (e.g., embedded systems, IoT) and for improving the reliability and security of deep learning systems in safety-critical applications.