  Transformer networks have emerged as the state-of-the-art approach for
natural language processing tasks and are gaining popularity in other domains
such as computer vision and audio processing. However, the efficient hardware
acceleration of transformer models poses new challenges due to their high
arithmetic intensities, large memory requirements, and complex dataflow
dependencies. In this work, we propose ITA, a novel accelerator architecture
for transformers and related models that targets efficient inference on
embedded systems by exploiting 8-bit quantization and an innovative softmax
implementation that operates exclusively on integer values. By computing
on-the-fly in streaming mode, our softmax implementation minimizes data
movement and energy consumption. ITA achieves competitive energy efficiency
with respect to state-of-the-art transformer accelerators with 16.9 TOPS/W,
while outperforming them in area efficiency with 5.93 TOPS/mm$^2$ in 22 nm
fully-depleted silicon-on-insulator technology at 0.8 V.
