Heterogeneous Computing for Artificial Intelligence Applications

In today’s era of rapidly growing AI workloads and data-intensive applications, heterogeneous computing has become essential for achieving high performance and energy efficiency. This course introduces the principles and practical techniques of heterogeneous computing, focusing on systems that combine CPUs, GPUs, and FPGAs to accelerate artificial intelligence (AI) and machine learning workloads. Students will explore how computation is distributed across heterogeneous architectures, how to design and implement hardware accelerators, and how to integrate them with CPUs using modern tools and frameworks. Through a combination of theory, design labs, and project-based learning, participants will gain hands-on experience developing CPU, GPU, and FPGA-based AI accelerators, optimizing data movement and parallelism, and deploying end-to-end AI inference pipelines.