郭宏晴,张盛兵,李楚曦,等.基于混合踪迹的智能处理器模型和评估分析[J]. 微电子学与计算机,2023,40(6):90-99. doi: 10.19304/J.ISSN1000-7180.2022.0475
引用本文: 郭宏晴,张盛兵,李楚曦,等.基于混合踪迹的智能处理器模型和评估分析[J]. 微电子学与计算机,2023,40(6):90-99. doi: 10.19304/J.ISSN1000-7180.2022.0475
GUO H Q,ZHANG S B,LI C X,et al. Mixed-trace based simulation model and evaluation for AI processor[J]. Microelectronics & Computer,2023,40(6):90-99. doi: 10.19304/J.ISSN1000-7180.2022.0475
Citation: GUO H Q,ZHANG S B,LI C X,et al. Mixed-trace based simulation model and evaluation for AI processor[J]. Microelectronics & Computer,2023,40(6):90-99. doi: 10.19304/J.ISSN1000-7180.2022.0475

基于混合踪迹的智能处理器模型和评估分析

Mixed-trace based simulation model and evaluation for AI processor

  • 摘要: 近年来,紧耦合智能处理器在资源受限的边缘侧智能处理器应用中受到了广泛关注.但是针对主协处理器在流水线耦合关系做早期设计空间探索时,存在硬件资源关系共享性,数据通路结构复杂多样化以及片上主协计算特征异构性的特点,使得针对智能处理器的仿真评估建模面临着挑战.本文针对紧耦合智能处理器的结构特点,将硬件结构抽象成为软件仿真模型框架,通过对主协处理器基本硬件资源分析,分解指令控制的不同数据通路,设计智能处理器仿真模型.将主处理器与智能协处理器分别采用踪迹仿真和模型解析的方法,引入混合踪迹记录时间戳以统计部件访问信息,结合基于解析的性能评估算法,实现对智能处理器的性能评估.实验结果表明,基于混合踪迹的智能处理器模型和评估分析可以有效的解出智能计算的实际执行结果,并评估得到硬件的性能,包括延时,能耗和功耗等重要参数.

     

    Abstract: In recent years, tightly coupled AI processors have received extensive attention in resource-constrained edge-side intelligent processor applications. But when doing early design space exploration for the main coprocessor in the pipeline coupling relationship. There are the characteristics of shared hardware resource relationship, complex and diverse data path structure, and heterogeneity of on-chip main-cooperative computing features. This makes the simulation evaluation modeling for AI processors facing challenges. This paper focuses on the structural characteristics of tightly coupled AI processors. Abstract the hardware structure into a software simulation model framework. By analyzing the basic hardware resources of the main coprocessor, the different data paths controlled by the instruction are decomposed, and the AI processor simulation model is designed. The main processor and the coprocessor are used in trace simulation and model analysis, respectively. Introduce hybrid trace record timestamps to count widget access information. Then, combined with the analytical performance evaluation algorithm, the performance evaluation of the AI processor is realized. The experimental results show that the AI processor model and evaluation analysis based on the hybrid trace can effectively solve the actual execution results of AI computing, and evaluate the performance of the hardware, including important parameters such as delay, energy and power.

     

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