Energy efficient spike transformer accelerator at the edge

Abstract Large language models are widely used across various applications owing to their superior performance. However, their high computational cost makes deployment on edge devices challenging. Spiking neural networks (SNNs), with their power-efficient, event-driven binary operations, offer a promising alternative. Combining SNNs and transformers is expected to be an effective solution for edge computing. This study proposes an energy-efficient spike transformer accelerator, which is the base component of the large language models, for edge computing, combining the efficiency of SNNs with the performance of transformer models. The design achieves performance levels comparable to traditional transformers while maintaining the lower power consumption characteristic of SNNs. To enhance hardware efficiency, a specialized computation engine and novel datapath for the spike transformer are introduced. The proposed design is implemented on the Xilinx Zynq UltraScale+ ZCU102 device, demonstrating significant improvements in energy consumption over previous transformer accelerators. It even surpasses some recent binary transformer accelerators in efficiency. Implementation results confirm that the proposed spike transformer accelerator is a feasible solution for running transformer models on edge devices.