  Transformers have revolutionized various real-world applications from natural
language processing to computer vision. However, traditional von-Neumann
computing paradigm faces memory and bandwidth limitations in accelerating
transformers owing to their massive model sizes. To this end, In-memory
Computing (IMC) crossbars based on Non-volatile Memories (NVMs), due to their
ability to perform highly parallelized Matrix-Vector-Multiplications (MVMs)
with high energy-efficiencies, have emerged as a promising solution for
accelerating transformers. However, analog MVM operations in crossbars
introduce non-idealities, such as stochastic read & write noise, which affect
the inference accuracy of the deployed transformers. Specifically, we find
pre-trained Vision Transformers (ViTs) to be vulnerable on crossbars due to the
impact of write noise on the dynamically-generated Key (K) and Value (V)
matrices in the attention layers, an effect not accounted for in prior studies.
We, thus, propose ClipFormer, a transformation on the K and V matrices during
inference, to boost the non-ideal accuracies of pre-trained ViT models.
ClipFormer requires no additional hardware and training overhead and is
amenable to transformers deployed on any memristive crossbar platform. Our
experiments on Imagenet-1k dataset using pre-trained DeiT-S transformers,
subjected to standard training and variation-aware-training, show >10-40%
higher non-ideal accuracies at the high write noise regime by applying
ClipFormer.
