Multi Accelerator

Multi-accelerator systems aim to optimize the execution of computationally intensive tasks, particularly deep neural networks (DNNs), by leveraging multiple specialized hardware units. Current research focuses on co-optimizing hardware architecture (including tensor/vector units and memory configurations) with efficient scheduling algorithms (like deep reinforcement learning) and data placement strategies across these accelerators to maximize throughput and energy efficiency for various models, including transformers and convolutional neural networks. This field is crucial for advancing artificial intelligence applications, particularly in resource-constrained environments, by enabling faster and more energy-efficient processing of large-scale models.