A 40nm 5.6TOPS/W 239GOPS/mm2 Self-Attention Processor with Sign Random Projection-based Approximation

Abstract

Transformer architecture is one of the most remarkable recent breakthroughs in neural networks, achieving state-of-the-art (SOTA) performance on various natural language processing (NLP) and computer vision tasks. Self-attention is the key enabling operation for transformer-based models. However, its quadratic computational complexity to the sequence length makes this operation the major performance bottleneck for those models. Thus, we propose a novel self-attention accelerator that skips most of the computation by utilizing an approximate candidate selection algorithm. Implemented in a 40nm CMOS technology, our 5.64 mm2 chip operates at 100–600 MHz consuming 48.3-685 mW to achieve the energy and area efficiency of 0.354-5.61 TOPS/W and 239 GOPS/mm2, respectively.