Early Area and Power Estimation Model for Rapid System Level Design and Design Space Exploration
Abhishek Narayan Tripathi, Arvind Rajawat
DOI: 10.15598/aeee.v20i1.4229
Abstract
Power and area estimation in the early stage of designing is very critical for a system. This paper presents the neural network-based early area and power estimation model. The flow starts with the training of the neural network model from the selected behavioral level parameters, which imposes to provide accurate estimations. The model accuracy is validated against ITC99 benchmark programs. The run-times are faster than the synthesis run-times. For the ASIC-based designs, the proposed model took 5 seconds, while Synopsys Design Compiler took 5 minutes. In terms of timing, the estimation speed is more than the order of magnitude faster than the conventional synthesis-based approach. The modeling methodology provides a better, accurate, and fast area and power estimations, at an early stage of the Very-Large-Scale Integration (VLSI) design. In addition, the model eliminates the need for synthesis-based exploration and provides the design picking before synthesis.