Heterogeneous Computing

Heterogeneous computing focuses on leveraging diverse hardware architectures (CPUs, GPUs, NPUs, FPGAs, etc.) to optimize the performance and energy efficiency of computationally intensive tasks, particularly in machine learning. Current research emphasizes efficient resource allocation and scheduling algorithms for various model architectures, including transformers, spiking neural networks, and large language models, across heterogeneous platforms, often employing techniques like model parallelism and split learning. This field is crucial for advancing machine learning applications in resource-constrained environments (edge computing) and large-scale deployments (cloud computing), enabling faster training and inference while reducing costs.