Heterogeneous Architecture

Heterogeneous architectures involve designing and utilizing computing systems composed of diverse hardware components (e.g., CPUs, GPUs, specialized accelerators) to optimize performance for specific tasks. Current research focuses on efficient mapping of workloads, particularly deep learning models (CNNs, Transformers, MLPs), onto these heterogeneous platforms, often employing techniques like knowledge distillation, reinforcement learning-based scheduling, and novel compiler optimizations to bridge architectural differences and maximize resource utilization. This research is crucial for improving the efficiency and scalability of computationally intensive applications in fields like robotics, machine learning, and high-performance computing, leading to faster processing and reduced energy consumption.