Existing binary Transformers are promising in edge deployment due to their
compact model size, low computational complexity, and considerable inference
accuracy.However, deploying binary Transformers faces challenges on prior
processors due to inefficient execution of quantized matrix multiplication
(QMM) and the energy consumption overhead caused by multi-precision
activations.To tackle the challenges above, we first develop a computation flow
abstraction method for binary Transformers to improve QMM execution efficiency
by optimizing the computation order.Furthermore, a binarized energy-efficient
Transformer accelerator, namely BETA, is proposed to boost the efficient
deployment at the edge.Notably, BETA features a configurable QMM engine,
accommodating diverse activation precisions of binary Transformers and offering
high-parallelism and high-speed for QMMs with impressive energy
efficiency.Experimental results evaluated on ZCU102 FPGA show BETA achieves an
average energy efficiency of 174 GOPS/W, which is 1.76~21.92x higher than prior
FPGA-based accelerators, showing BETA's good potential for edge Transformer
acceleration.

通过优化计算顺序和引入高度并行性和高速的 QMM 引擎，本文提出了一种基于二进制 Transformer 的能效较高的加速器 BETA，实验证明，BETA 在边缘部署中能够实现 174 GOPS/W 的平均能效，比之前基于 FPGA 的加速器高出 1.76~21.92 倍，显示了 BETA 在边缘 Transformer 加速方面的巨大潜力。