the main idea of Accelerating atention mechanism on FPGAs based on eficient reconfigurable systolic array

The main idea of the paper is to accelerate attention mechanism on FPGAs using an efficient reconfigurable systolic array. The authors propose a novel approach that leverages the parallelism and flexibility of FPGAs to achieve high performance and energy efficiency for attention-based neural networks. They utilize a systolic array architecture that is optimized for matrix multiplication, which is the core operation in attention calculations. By reconfiguring the systolic array, they can efficiently handle different attention types and sizes. The authors demonstrate the effectiveness of their approach by implementing it on an FPGA platform and comparing it to state-of-the-art solutions. The results show that their approach achieves significant speedup while reducing energy consumption, making it a promising solution for accelerating attention mechanisms in neural networks