An Energy-Efficient Transformer Fine-Tuning Processor for Personalized Edge Applications

Transformer models have achieved remarkable success in various domains. Given concerns about user privacy, there is an urgent need for on-device fine-tuning of Transformer models at the edge. Transformer fine-tuning faces three key challenges: 1)$O(n^{3})$re-computations during BP/WG save only$O(n^{2})$storage, limiting batch size for fine-tuning speedup. 2)Weakly related tokens account for 87.9% of computations but contribute only 8.7% to accuracy. 3)89.1% of multiplications in matrix multiplications (MM) involve dual near-zero operands, leading to a$1.9\times $increase in logic toggling energy due to frequent exponent/mantissa variations near zero. This paper proposes a Transformer-based processor supporting energy-efficient fine-tuning with three key features to tackle the above challenges. 1)An exponent-stationary re-computing scheduler (ESRS) reduces 44.2% of the storage requirement for each batch. 2)An aggressive linear fitting unit (ALFU) saves 47.4% of the computations in each iteration. 3)A logarithmic domain processing element (LDPE) decreases 36.3% of energy for MM in fine-tuning. Fabricated with 22nm technology, the proposed processor has an area of 6.4 mm2. The proposed Transformer processor achieves a peak energy efficiency of 54.94 TFLOPS/W. It reduces fine-tuning energy by$4.27\times $and offers$3.57\times $speedup for GPT-2.