Cao, T., Yu, W., Gao, Y., Liu, C., Zhang, T., Yan, S., & Goh, W. L. (2025). Edge PoolFormer: Modeling and Training of PoolFormer Network on RRAM Crossbar for Edge-AI Applications. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 33(2), 384–394. https://doi.org/10.1109/tvlsi.2024.3472270
Abstract:
PoolFormer is a subset of Transformer neural network with a key difference of replacing computationally demanding token mixer with pooling function. In this work, a memristor-based PoolFormer network modeling and training framework for edge-AI applications is presented. The original PoolFormer structure is further optimized for hardware implementation on RRAM crossbar by replacing the normalization operation with scaling. In addition, the non-idealities of RRAM crossbar from device to array level as well as peripheral readout circuits are analyzed. By integrating these factors into one training framework, the overall neural network performance is evaluated holistically and the impact of nonidealities to the network performance can be effectively mitigated. Implemented in Python and PyTorch, a 16-block PoolFormer network is built with 64×64 4-level RRAM crossbar array model extracted from measurement results. The total number of the proposed Edge PoolFormer network parameters is 0.246M, which is at least one order smaller than the conventional CNN implementation. This network achieved inference accuracy of 88.07% for CIFAR-10 image classification tasks with accuracy degradation of 1.5% compared to the ideal software model with FP32 precision weights.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR), Singapore - High Linearity Silicon Germanium Photonic Modulator for 6G Analog Radio over Fiber Project
Grant Reference no. : M24M8b0004
This research / project is supported by the Agency for Science, Technology and Research (A*STAR), Singapore - Nanosystems at the Edge
Grant Reference no. : A18A1b0055