Deploying the Internet of Things and machine learning (ML)-based applications increased processing rate and data transfer between main memory and processing elements (PEs) in NoC-based communication networks, leading to memory access problems. Predicting and identifying reusable data for different tasks can reduce memory accesses and support various applications with high flexibility. Therefore, we propose a method to minimize memory access. It provides a predictor circuit to assign the address for PEs based on data buffering into task cores due to their reusability. We also present a delay-aware algorithm to investigate the initial relationship between tasks and identify a similar pattern for the mapped task graph on the various topologies. Our algorithm and predictor circuit decrease latency for determining related data to tasks and transfers data from global buffer onto PEs and buffers them according to its reusability for tasks with similar patterns. We utilized real data of the reported COVID-19 statistics and particulate matter 2.5 (PM_2.5) condensation for evaluating our method. Simulation results demonstrate reducing energy consumption, delay, memory access, and increasing area consumption by approximately 61.83%, 39.96%, 66.66%, and 0.13%, respectively, for the mapped task graphs on a mesh network before employing the circuit and algorithm.

部署物联网和基于机器学习 (ML) 的应用程序提高了基于 NoC 的通信网络中主存储器和处理元件 (PE) 之间的处理速率和数据传输，从而导致内存访问问题。预测和识别不同任务的可重用数据可以减少内存访问并以高灵活性支持各种应用程序。因此，我们提出了一种最小化内存访问的方法。由于 PE 的可重用性，它提供了一个预测器电路，用于根据数据缓冲到任务核心中为 PE 分配地址。我们还提出了一种延迟感知算法来研究任务之间的初始关系，并为各种拓扑上的映射任务图确定类似的模式。我们的算法和预测器电路减少了确定任务相关数据的延迟，并将数据从全局缓冲区传输到 PE 并根据其对具有相似模式的任务的可重用性来缓冲它们。我们利用了报告的 COVID-19 统计数据和颗粒物 2.5 (PM_2.5）用于评估我们方法的冷凝。仿真结果表明，在采用电路和算法之前，网状网络上的映射任务图分别减少了约 61.83%、39.96%、66.66% 和 0.13% 的能耗、延迟、内存访问和增加的面积消耗。