Accelerator Architecture

Accelerator architecture research focuses on designing efficient hardware for executing computationally intensive machine learning models, primarily aiming to minimize latency and energy consumption while maximizing throughput. Current efforts concentrate on optimizing architectures for specific model types, such as convolutional neural networks (CNNs) and transformers, often incorporating techniques like sparsity exploitation, quantization, and innovative memory structures (e.g., content-addressable memory). These advancements are crucial for deploying AI applications on resource-constrained edge devices and high-performance computing systems, impacting fields ranging from autonomous driving to natural language processing.