Reasoning high-level abstractions from bit-blasted Boolean networks (BNs) such as gate-level netlists can significantly benefit functional verification, logic minimization, datapath synthesis, malicious logic identification, etc. Mostly, conventional reasoning approaches leverage structural hashing and functional propagation, suffering from limited scalability and inefficient usage of modern computing power. In response, we propose a novel symbolic reasoning framework exploiting graph neural networks (GNNs) and GPU acceleration to reason high-level functional blocks from gate-level netlists, namely Gamora, which offers high reasoning performance w.r.t exact reasoning algorithms, strong scalability to BNs with over 33 million nodes, and generalization capability from simple to complex designs. To further demonstrate the capability of Gamora, we also evaluate its reasoning performance after various technology mapping options, since technology-dependent optimizations are known to make functional reasoning much more challenging. Experimental results show that (1) Gamora reaches almost 100% and over 97% reasoning accuracy for carry-save-array (CSA) and Booth-encoded multipliers, respectively, with up to six orders of magnitude speedups compared to the state-of-the-art implementation in the ABC framework; (2) Gamora maintains high reasoning accuracy (>92%) in finding functional modules after complex technology mapping, upon which we comprehensively analyze the impacts on Gamora reasoning from technology mapping.
翻译:多数情况下,传统推理方法利用了结构散列和功能传播,而现代计算能力的可缩放程度有限且使用效率低下。 作为回应,我们提议了一个创新的象征性推理框架,利用图形神经网络(GNN)和GPU加速,以解释门级网络列表(即Gamora)的高功能区块,即Gamora,它提供高推理精确推理算法,向3 300多万节点以上的BN提供很强的伸缩性,以及从简单到复杂的设计的一般化能力。为了进一步展示Gamora的能力,我们还评估了各种技术绘图选项之后的推理性能,因为人们知道以技术为基础的优化使功能推理更具挑战性。实验结果显示:(1) Gamora 达到近100%和超过97%的推理精确度,用于承载阵列(CSA)和BUBE编码的推算推算器,分别提供了高推理性性推算法的推算法,在BC的推理学模型中维持了六级推理学级的推算,在BC的推算中分别维持了六级后推理学级的推理学级,在BC的推算模型中,在推理学中分别维持了6级推理学的推算。</s>