Jincheng Miao developed a performance optimization for the huggingface/optimum-habana repository by integrating the FusedSDPA kernel into the Bert model’s self-attention mechanism, specifically targeting Habana Gaudi accelerators. This work replaced the standard scaled dot-product attention in BertSdpaSelfAttention.forward, focusing on improving inference throughput and reducing latency in non-training scenarios. Using Python and leveraging deep learning expertise, Jincheng validated the performance gains on Habana hardware and maintained clear traceability through a dedicated commit. The project demonstrated depth in transformer model optimization and hardware-specific tuning, addressing the need for efficient inference on specialized accelerators within the deep learning ecosystem.