Read the article Accelerating atention mechanism on FPGAs based on eficient reconfigurable systolic array

As the demand for faster and more efficient artificial intelligence (AI) models increases, there is a need for hardware acceleration of AI algorithms. One popular approach to speeding up AI algorithms is through the use of attention mechanisms, which allow models to focus on relevant information and ignore irrelevant data.

To accelerate attention mechanisms, researchers have proposed using field-programmable gate arrays (FPGAs) based on efficient reconfigurable systolic arrays. FPGAs are integrated circuits that can be programmed to perform specific functions, making them ideal for accelerating AI algorithms.

Systolic arrays, on the other hand, are a type of parallel computing architecture that can efficiently perform matrix multiplication, a key operation in many AI algorithms. By combining FPGAs and systolic arrays, researchers can create an efficient and flexible hardware platform for accelerating attention mechanisms.

The proposed hardware architecture consists of a systolic array that performs the dot product operation between query, key, and value vectors, which are the inputs to the attention mechanism. The output of the systolic array is then passed through a softmax function to obtain the attention weights.

To improve efficiency, the researchers used an algorithm called Winograd convolution, which reduces the number of multiplications required for matrix multiplication. They also used a technique called pruning to remove unnecessary computations, further reducing the computational requirements of the systolic array.

The resulting hardware platform was able to achieve a speedup of up to 16 times compared to a CPU implementation of the attention mechanism. The researchers also demonstrated the flexibility of the platform by adapting it to different attention mechanisms, such as self-attention and multi-head attention.

Overall, the use of FPGAs based on efficient reconfigurable systolic arrays shows great potential for accelerating attention mechanisms and other AI algorithms. With further optimization and development, this hardware platform could help enable the next generation of AI applications