Update README.md

README.md

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 # ChiPBench-D
+ChiPBench:Benchmarking End-to-End Performance of AI-based Chip Placement Algorithms
+
+
+Chip placement is a critical step in the Electronic Design Automation (EDA) workflow, which aims to arrange chip modules on the canvas to optimize the performance, power, and area (PPA) metrics of final designs.  
+Recent advances show great potential of AI-based algorithms in chip placement.  
+However, due to the lengthy EDA workflow, evaluations of these algorithms often focus on _intermediate surrogate metrics_, which are computationally efficient but often misalign with the final _end-to-end performance_ (i.e., the final design PPA).  
+To address this challenge, we propose to build **ChiPBench**, a comprehensive benchmark specifically designed to evaluate the effectiveness of AI-based algorithms in final design PPA metrics.  
+Specifically, we generate a diverse evaluation dataset from 20 circuits across various domains, such as CPUs, GPUs, and NPUs.  
+
+We present an end-to-end evaluation workflow for placement stages of the EDA flow.  
+To evaluate a stage-specific algorithm, the output from the preceding stage serves as its input, and the algorithm's output is reintegrated into the original design flow.  
+Final PPA metrics provide a comprehensive assessment, avoiding the limitations of isolated stage-specific metrics.  
+This approach facilitates algorithm optimization by ensuring improvements translate into practical chip design enhancements.  
+We believe ChiPBench will effectively bridge the gap between academia and industry.
+
 
 This project represents the dataset part of ChiPBench. The code can be found on GitHub: [ChiPBench](https://github.com/MIRALab-USTC/ChiPBench).