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Accelerating Data Transfer in Dataflow Architectures Through a Look-Ahead Acknowledgment Mechanism

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Abstract

The dataflow architecture, which is characterized by a lack of a redundant unified control logic, has been shown to have an advantage over the control-flow architecture as it improves the computational performance and power efficiency, especially of applications used in high-performance computing (HPC). Importantly, the high computational efficiency of systems using the dataflow architecture is achieved by allowing program kernels to be activated in a simultaneous manner. Therefore, a proper acknowledgment mechanism is required to distinguish the data that logically belongs to different contexts. Possible solutions include the tagged-token matching mechanism in which the data is sent before acknowledgments are received but retried after rejection, or a handshake mechanism in which the data is only sent after acknowledgments are received. However, these mechanisms are characterized by both inefficient data transfer and increased area cost. Good performance of the dataflow architecture depends on the efficiency of data transfer. In order to optimize the efficiency of data transfer in existing dataflow architectures with a minimal increase in area and power cost, we propose a Look-Ahead Acknowledgment (LAA) mechanism. LAA accelerates the execution ow by speculatively acknowledging ahead without penalties. Our simulation analysis based on a handshake mechanism shows that our LAA increases the average utilization of computational units by 23.9%, with a reduction in the average execution time by 17.4% and an increase in the average power efficiency of dataflow processors by 22.4%. Crucially, our novel approach results in a relatively small increase in the area and power consumption of the on-chip logic of less than 0.9%. In conclusion, the evaluation results suggest that Look-Ahead Acknowledgment is an effective improvement for data transfer in existing dataflow architectures.

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Authors and Affiliations

  1. State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Yu-Jing Feng, Xu Tan, Xiao-Chun Ye, Dong-Rui Fan, Wen-Ming Li, Da Wang, Hao Zhang & Zhi-Min Tang

  2. Beijing Smartchip Microelectronics Technology Company Limited, Beijing, 100000, China

    De-Jian Li

  3. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, 100190, China

    Dong-Rui Fan

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  1. Yu-Jing Feng
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Correspondence to Xiao-Chun Ye.

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Feng, YJ., Li, DJ., Tan, X. et al. Accelerating Data Transfer in Dataflow Architectures Through a Look-Ahead Acknowledgment Mechanism. J. Comput. Sci. Technol. 37, 942–959 (2022). https://doi.org/10.1007/s11390-020-0555-6

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