W-AMA: Weight-aware Approximate Multiplication Architecture for neural processing

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@Article{LIU:2023:compeleceng,

• author = "Bo Liu2 and Renyuan Zhang and Qiao Shen and Zeju Li and Na Xie and Yuanhao Wang and Chonghang Xie and Hao Cai",
• title = "{W-AMA:} Weight-aware Approximate Multiplication Architecture for neural processing",
• journal = "Computers and Electrical Engineering",
• volume = "111",
• pages = "108921",
• year = "2023",
• ISSN = "0045-7906",
• DOI = "doi:10.1016/j.compeleceng.2023.108921",
• URL = "https://www.sciencedirect.com/science/article/pii/S0045790623003452",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Approximate computing, Deep Neural Network, ANN, Hardware accelerator, Average Hessian trace",
• abstract = "This paper presents the Weight-aware Approximate Multiplication Architecture (W-AMA) for Deep Neural Networks (DNNs). Considering the Gaussian-like weight distribution, it deploys an accuracy-configurable computing component to improve the computational efficiency. Two techniques for effectively integrating the W-AMA into DNN accelerator are presented: (1) A Cartesian Genetic Programming (CGP) based approximate multiplier is designed and selectable to compute the Least Significant Bit (LSB) for a higher accuracy mode. The Reward-Penalty-Coefficient (RPC) is proposed to achieve the internal-compensation. (2) The Hessian-Aware-Approximation (HAA) method is used for hybrid approximate modes cross-layer mapping. Based on the W-AMA, an energy-efficient DNN accelerator is proposed and evaluated on 28 nm technology. It can achieve the energy efficiency of 9.6 TOPS/W, and the computational energy efficiency can be improved by 1.5times compared with the standard units, with an 0.52percent accuracy loss on CIFAR-10 using ResNet-18",

}

Genetic Programming entries for Bo Liu2 RenYuan Zhang Qiao Shen Zeju Li Na Xie Yuanhao Wang Chonghang Xie Hao Cai

Citations