Layer-Wise Mixed-Modes CNN Processing Architecture With Double-Stationary Dataflow and Dimension-Reshape Strategy

作者：Bo Liu, Xinxiang Huang, Yang Zhang, Guang Yang, Han Yan, Chen Zhang, Z. Y. Li, Yuanhao Wang, Hao Cai · 发表于：IEEE Transactions on Circuits and Systems I Regular Papers · 年份：2024 · DOI：10.1109/tcsi.2024.3434706 · 被引用次数：11 · 研究领域：CCD and CMOS Imaging Sensors、Advanced Memory and Neural Computing、Advanced Neural Network Applications

With the development of convolutional neural networks (CNN) across various domains, the growth in network structure complexity and computational load has increasingly become a research focus in the deployment of neural networks. The key to current research on neural network accelerators lies in striking a balance between computational accuracy and energy efficiency. This paper proposes a software-hardware co-design to strike the balance for CNN edge applications. On the hardware side, a 3-dimensional tensor engine (3D-TE), achieved with reconfigurable Tensor Processing Units (TPUs), is introduced for efficient convolution computation. We optimize the CNN dataflow on 3D-TE using a dimension reshaping method for feature maps rearrangement, and a double stationary dataflow scheduling to reduce memory access. This paper adopts a configurable approximate multiplier design based on Boolean Matrix Factorization (BMF) based logic synthesis applied in the architecture of TPU. The proposed 3D-TE, characterized by its configurable precision, enables the TPUs to dynamically adapt the bitwidth of features and weights in response to varying precision requirements. On the software side, a hessian-guided layer precision mapping is adopted to reduce unnecessary computational overhead, and a progressive re-training approach is proposed to enable a better approximation configuration and higher power reduction. Fabricated on 28-nm CMOS, this work achieves an optimized energy efficiency of 14.9 T...