GRIPT: Graph Attention-Assisted Inductive Methodology for Fast and Accurate Average Power Estimation From RTL Simulation Skipping Gate-Level Simulation

This paper proposes GRIPT, a novel graph attention-based inductive methodology that enables a fast and accurate average power estimation of synthesized ASIC Design from RTL simulation, bypassing gate-level simulation. The proposed GRIPT methodology with features-aided attention mechanism-based inductive Graph Neural Network (GNN) model propagates the input wires’ toggle rates acquired from the RTL SAIF file through the circuit. We examine the proposed GRIPT methodology’s versatility by testing circuits across technology nodes and foundries like TSMC 65nm, 40nm, 90nm, 130nm and GF 40nm while training exclusively on the TSMC 65nm technology node. We introduce unseen and untrained logic cells to test the transferability of the proposed GRIPT model in toggle rate prediction across technology nodes. We evaluate the scalability of the proposed GRIPT by testing circuits of considerable size, like circuit trigonometry from OpenCores, and circuits arbiter, multiplier, div, log2, sin, square and sqrt from the EPFL benchmark suite, without training them. The proposed GRIPT surpasses the state-of-the-art GRANNITE model in predicting the unseen and untrained logic cell’s toggle rates to justify the proposed GRIPT’s transferability for designs across technology nodes to achieve an average improvement of 6.74%, 5.53%, 8.93%, 6.88% and 7.22%, respectively. To demonstrate versatility and scalability, the proposed GRIPT methodology outperforms the commercial RTL power estimation tool and GRANNITE in estimating the average power of circuits spanning TSMC 65nm, 40nm, 90nm, 130nm, and GF 40nm technology nodes, with a mean improvement of 23.42%, 16.59%, 16.35%, 27.16%, and 28.87%; 2.01%, 0.97%, 0.98%, 1.6% and 2.72%, respectively. The proposed GRIPT is 12.57X and 1.1X faster, with an average inference throughput (number of cycles inferred per second) of 1384.6 Hz compared to the commercial gate-level power estimation tool and GRANNITE, respectively.