Multiterminal Pathfinding in Practical VLSI Systems with Deep Neural Networks
Multiterminal Pathfinding in Practical VLSI Systems with Deep Neural Networks
复制标题
DOI：
10.1145/3564930
复制
发表时间：
2022-01
影响因子：
1.4
通讯作者：
Dmitry Utyamishev;Inna Partin-Vaisband
中科院分区：
文献类型：
--
作者：
Dmitry Utyamishev;Inna Partin-Vaisband
提出了一种多终端避障寻径方法。这种方法的灵感来自深度图像学习。其核心思想是基于训练条件生成对抗网络(CGAN)来将寻路任务解释为图形位图，从而将一个寻路任务映射到由另一个位图表示的寻路解上。为了实现cGAN寻路，还提出了一种生成合成数据集的方法。CGAN模型在PYTHON/KERAS上实现，在综合生成的数据上进行训练，在实际的VLSI基准上进行评估，并与最先进的标准进行比较。由于在GPU硬件上进行了有效的并行化，对于中等复杂的寻路任务，所提出的方法产生了最先进的有线长度以及更好的运行时间和吞吐量。然而，随着任务复杂性的增加，所提出的方法的运行时间和吞吐量保持不变，有望在复杂的寻路任务中比最先进的方法提高数量级。CGAN探路器可用于许多高吞吐量应用，如复杂VLSI系统中的导航、跟踪和布线。最后一项对这项工作特别感兴趣。
A multiterminal obstacle-avoiding pathfinding approach is proposed. The approach is inspired by deep image learning. The key idea is based on training a conditional generative adversarial network (cGAN) to interpret a pathfinding task as a graphical bitmap and consequently map a pathfinding task onto a pathfinding solution represented by another bitmap. To enable the proposed cGAN pathfinding, a methodology for generating synthetic dataset is also proposed. The cGAN model is implemented in Python/Keras, trained on synthetically generated data, evaluated on practical VLSI benchmarks, and compared with state-of-the-art. Due to effective parallelization on GPU hardware, the proposed approach yields a state-of-the-art-like wirelength and a better runtime and throughput for moderately complex pathfinding tasks. However, the runtime and throughput with the proposed approach remain constant with an increasing task complexity, promising orders of magnitude improvement over state-of-the-art in complex pathfinding tasks. The cGAN pathfinder can be exploited in numerous high throughput applications, such as, navigation, tracking, and routing in complex VLSI systems. The last is of particular interest to this work.