Dataflow Accelerator

Dataflow accelerators are specialized hardware architectures designed to efficiently process the computationally intensive tasks inherent in deep learning models, primarily by pipelining computations across multiple processing elements. Current research focuses on optimizing dataflow architectures for various neural network types, including convolutional neural networks (CNNs) and large language models (LLMs), often employing techniques like layer-specific hardware customization, runtime reconfiguration, and efficient memory management (e.g., using high-bandwidth memory and smart off-chip eviction). These advancements aim to improve the speed, energy efficiency, and scalability of deep learning inference and training, impacting fields ranging from computer vision and natural language processing to medical image analysis.