  Quantization is wildly taken as a model compression technique, which obtains
efficient models by converting floating-point weights and activations in the
neural network into lower-bit integers. Quantization has been proven to work
well on convolutional neural networks and transformer-based models. Despite the
decency of these models, recent works have shown that MLP-based models are able
to achieve comparable results on various tasks ranging from computer vision,
NLP to 3D point cloud, while achieving higher throughput due to the parallelism
and network simplicity. However, as we show in the paper, directly applying
quantization to MLP-based models will lead to significant accuracy degradation.
Based on our analysis, two major issues account for the accuracy gap: 1) the
range of activations in MLP-based models can be too large to quantize, and 2)
specific components in the MLP-based models are sensitive to quantization.
Consequently, we propose to 1) apply LayerNorm to control the quantization
range of activations, 2) utilize bounded activation functions, 3) apply
percentile quantization on activations, 4) use our improved module named
multiple token-mixing MLPs, and 5) apply linear asymmetric quantizer for
sensitive operations. Equipped with the abovementioned techniques, our Q-MLP
models can achieve 79.68% accuracy on ImageNet with 8-bit uniform quantization
(model size 30 MB) and 78.47% with 4-bit quantization (15 MB).
