DiMO-CNN: Deep Learning Toolkit-Accelerated Analytical Modeling and Optimization of CNN Hardware and Dataflow

作者：Jianfeng Song, Rongjian Liang, Bo Yuan, Jiang Hu · 发表于：IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 年份：2024 · DOI：10.1109/tcad.2024.3429419 · 被引用次数：4 · 研究领域：Neural Networks and Applications、Advanced Neural Network Applications

The growing complexity of CNNs demands both hardware acceleration design and dataflow mapping solutions. The large co-design solution space presents a huge challenge. We introduce an analytical model for assessing CNN hardware design and dataflow solutions, using a matrix-based approach. Our co-optimization method, combining nonlinear programming and parallel local search, excels in addressing the power-performance-area tradeoff. The average relative error of our analytical model compared with Timeloop is as small as 1%. Compared to state-of-the-art methods, our co-optimization achieves solutions with average$3.14\times $shorter inference latency,$\mathbf {68.2\%}$less power consumption, and$\mathbf {74\%}$less area on all testcases. It also provides a$200\times $speedup of optimization runtime.