一、介绍
ECO algorithms, particularly in functional ECO, involve numerous stages, each of which requires selecting the optimal result from a set of potential candidates. These stages can include processes like key point mapping, where the algorithm must compare hundreds of key point pairs between the reference netlist and the implementation netlist to identify the correct mapping. Similarly, during port inversion checking, the algorithm must determine whether the phase of hundreds of ports has been inverted.
ECO算法，尤其在功能ECO领域，涉及许多阶段，每个阶段都需要从一组潜在候选结果中选择最佳结果。这些阶段也包括关键点映射（keypoint mapping），算法必须比较参考网表和实现网表之间的数百个关键点对（keypoint pairs），以确定正确的mapping关系。同样，在端口反相检查时，算法必须确定数百个端口的相位是否有反相。
These operations are computationally intensive and not easily parallelized, leading to significant processing times. This is where AI comes into play, offering a solution by extracting meaningful patterns from historical ECO data. By learning from past decisions, AI can provide informed guidance during the decision-making process, greatly enhancing efficiency.
这些操作是计算密集型的，不容易并行化，从而导致处理时间很长。这就是AI发挥作用的地方，ai提供了一个解决方案，从历史ECO数据中提取有意义的pattern。通过从过去的决策中学习，AI可以在新决策时提供智能的指导，从而大大提高效率。
二、利用Vector数据库获取ECO历史数据
To further streamline the ECO process, AI models can convert historical ECO data into a vector database, which stores and indexes data in a way that captures relationships and patterns between ECO decisions in past projects. At each stage of the ECO process, the algorithm can query this vector database instead of randomly processing potential candidates.
为了进一步简化ECO流程，AI模型可以以捕获过去项目中ECO决策之间的关系和pattern，并将历史ECO数据转换为矢量数据库存储和索引数据。在ECO过程的每个阶段，该算法都可以查询此向量数据库，而不是随机处理潜在的候选结果。
For instance, during key point mapping, the algorithm can consult the vector database, allowing the AI model to assess and score each candidate pair based on historical data. This scoring enables the ECO algorithm to rerank candidates, starting with those most likely to be optimal. Since most ECO stages allow the process to stop as soon as the best candidate is found, this drastically reduces computation time. As shown in Figure 1, the ECO algorithm consults the AI model at each decision point, and the AI model rapidly provides a probability score for each query, which is far quicker than running the full candidate process.
例如，在关键点映射期间，算法可以查阅向量数据库，允许AI模型根据历史数据评估和评分每个候选mapping对。此评分使ECO算法能够对候选项进行重新排序，从最有可能成为最佳候选项的候选项开始。由于大多数ECO阶段都允许流程在找到最佳候选项后立即停止，因此这大大减少了计算时间。如下图所示，ECO算法在每个决策点咨询AI模型，AI模型快速为每个查询提供概率分数，这比运行完整的候选流程要快得多。
Additionally, many ECO stages are bound by a timer. When the timer expires, the algorithm may be forced to select a candidate that is not the most optimal simply to meet timing constraints. AI can mitigate this risk by ensuring that the decision-making process is more efficient, helping the algorithm reach an optimal solution before the timer runs out. As a result, AI not only accelerates the ECO process but also improves the overall quality of the decisions made.
此外，许多ECO阶段都有运行超时的限制。当超时了，算法可能被迫选择一个不是最佳候选结果，只是为了满足这种运行时间的限制。AI可以通过确保决策过程更高效来降低这种风险，帮助算法在运行时间用完之前达到最佳解决方案。因此，AI不仅加速了ECO流程，还提高了所做决策的整体质量。
三、总结
Incorporating AI into the ECO decision-making process presents a significant advancement in optimizing functional ECO workflows. By leveraging AI models and vector databases built from historical ECO data, the algorithm can make more informed and efficient decisions at each stage. This not only accelerates the process by prioritizing the most promising candidates but also improves the overall quality of the results by reducing reliance on time-based cutoffs. As the complexity of ECO tasks continues to grow, AI's role in streamlining these processes will only become more crucial.
将AI纳入ECO决策过程是优化功能ECO工作流程的重大进步。通过利用从历史ECO数据构建的AI模型和矢量数据库，该算法可以在每个阶段做出更明智、更高效的决策。这不仅通过优先考虑最有前途的候选结果来加快流程，而且通过减少对运行时间的限制，提高了结果的整体质量。随着ECO 任务的复杂性不断增加，AI在简化这些流程方面的作用只会变得更加重要。
欢迎订阅LinkedIn专栏：【Functional Netlist ECO】
介绍：This newsletter will provide an in-depth understanding of functional netlist ECO
URL：https://www.linkedin.com/newsletters/functional-netlist-eco-7043272320538341376
