Impromptu Data Extraction and Analysis Data Mining and Analytics Framework for VLSI Designs

1. idΣa Impromptu Data Extraction and Analysis Data Mining and Analytics Framework for VLSI Designs Sandeep P (sandeep.p@intel.com); +91 80 2507 5492 Anand Ananthanarayanan (anand.ananthanarayanan@intel.com); +91 80 2507 5774 Intel Corporation

2. Author Affiliations

3. Abstract • Design processes from Logic design, validation, backend implementation and verification require a plethora of CAD tools. These tools generate reports, debug information in its own form and content. Designers need to parse and review data from multiple sources and tools to make design calls. When implementing backend design, a designer or a methodology owner needs to understand the patterns seen in the design. Data like number of paths dominated by low leakage, Slope profile for cells with margin > x ps, Drive strength profile of cells in timing path, etc., are critical to make design decisions, optimize design collaterals and ensure design with robust electrical functionality. Many of the data can be obtained only through data mining of results and logs of multiple tools. Data mining is also a constant activity from technology readiness to execution and post silicon debug phases. Data mining problem gets compounded when data is needed from different PV domains. For example, a designer looking to optimize power would need dynamic power information, path margin and max cap information all generated by different tools in different formats in different locations. Data mining has been historically done by adhoc scripts to parse through different reports, and log files. Data generated is post processed and then visualized. Any requirement change in data mining would need changes in the scripts. There is no data mining model which supports multiple tools with different output formats. There is no methodology which supports cross domain analysis. We present IDEA (Impromptu Data Extraction and Analysis). IDEA is data mining and data analysis framework in a highly interactive web application platform. It supports assimilating data from different tools and formats into one data organization in the form of SQL tables. SQL enables compact organization and faster queries. IDEA framework is built using the Linux-Apache™-Mysql™-Perl (LAMP) packages and uses the R language for performing statistical analysis on the data. R language enables handling huge amount of data with support for different statistical plots like pie-charts, histograms, box plots, scatter plots, Linear regression etc. IDEA data mining completes in minutes compared to hours/days with conventional approaches like scripts. IDEA is highly interactive web application with all the data extraction and plotting functionalities abstracted using highly interactive widgets. IDEA has been used to data mine power savings post Optimization, Analysis of power distribution, Profile the speed paths, Review standard cells usage, Utilization of cell sizes across the design space, RC delays per path stage and has multiple other usages. Large precious unorganized data lies unexploited. Structured Data Mining essential for competitive VLSI design. Increasing complexity makes data analytics a must-have for quality design. No EDA tool exists today to do this critical data mining. IDEA fills this gap and provides valuable data mining capability. It is time to think of Data Mining as a EDA product

4. Design Reports Design Process Timing Reports Extraction reports Cell utilization Route utilization DRC reports Layout Checks Multiple Tools Multiple Reports Multiple Formats Large Data gets generated requiring interpretation and Analysis

5. Design Quality Increasingly Dependent on Multiple Parameters

6. Data Mining - A Constant Activity Formal Data Mining Tool or Model Not Currently Available In Industry

7. To solve this Data Mining problem, we present idΣa

8. IDEA • Impromptu Data Extraction and Analysis (IDEA) is • Web application for Data mining on an open architecture • Linked Data Caching SQL databases • Common Xml interface for data manipulation • Statistical analysis capability with ‘R’ Language • Practically unlimited capacity with ‘R’ Language • Data visualization capability • Histograms, pie charts, density/scatter plots, dot charts • Faster turn around time (no text parsing scripts) • Intuitive, web based user interface Highly Interactive Application for Data Mining

9. IDEA Web Based Data Mining Platform

10. IDEA Architecture Application Tier DataBase Presentation Tier Storage Tier Three Tiered Web Application

11. Architecture – Idea Client Data Extraction Control Center Data Manipulation Data Viewer Experiments AJAX Calls JSON for data transfer Idea Server Apps Statistical Analysis Spreadsheet Generation PDF Converter Report Viewer Simple Client with Powerful Capabilities

12. Basic Usage Flow

13. IDEA Usage and Benefits - Data Mining Simplified -

14. Summary • Large precious unorganized data lies unexploited • Structured Data Mining essential for competitive VLSI design • Increasing complexity makes data analytics a must-have for quality design • No EDA tool exists today to do this critical data mining • IDEA fills this gap and provides valuable data mining capability - It is time to think of Data Mining as a EDA product -

15. Acknowledgements • Everyone at Intel who contributed to this work