Accelerating Transformer Models: FPGA-Based Hardware Optimization and Heterogeneous Computing Strategies

The Transformer model has gained widespread application in natural language processing (NLP) and computer vision due to its remarkable global dependency modeling capability. However, its large parameter size and high computational complexity pose significant challenges for resource-constrained hardware platforms. This paper thoroughly analyzes the advantages and limitations of FPGA as a hardware acceleration platform, highlighting its potential in low-power, low-latency inference through its flexible architecture and parallel processing capabilities. At the same time, it reveals the bottlenecks caused by storage capacity and loading efficiency in deploying Transformer models. By leveraging techniques such as model compression, quantization, and dataflow optimization, the loading performance of FPGA is significantly improved. Furthermore, this paper explores the collaborative mechanisms of FPGA within heterogeneous computing systems, demonstrating its capability to work alongside CPUs and GPUs in addressing complex AI tasks. Against the backdrop of limitations associated with traditional general-purpose chips, this paper examines the optimized design of application-specific chips (such as Sohu chips) and their performance advantages in Transformer tasks, while pointing out their lack of flexibility. To address the computational bottlenecks of the self-attention mechanism in Transformer models, an optimization method combining relative position encoding is proposed, effectively reducing complexity and enhancing model performance. By integrating model and hardware in a deeply synergistic manner, this paper provides theoretical foundations and practical guidance for the efficient deployment of Transformer models, opening up new possibilities for applications in edge computing and embedded systems.