Young-Ho Gong

LayUp: Layer-wise Parallelization for Energy-Efficient Edge LLM Training Exploiting Unified Memory Characteristics

This paper proposes LayUp, a layer-wise training optimization framework for edge devices with unified memory architectures that achieves speedup and energy reduction compared to baseline GPU-only training for GPT-2 models on the NVIDIA Jetson Orin NX, while conventional offloading increases latency.