The Big Data technology has evolved to handle both volume and velocity of data, currently being generated by the chip design and verification activities. However, we feel that the core challenge of effective data management and hence actionable insight generation is still not available to the industry in the true sense. Connecting data islands as created by various tools in various formats across digital design and verification workflows and creating a Unified Data Lake is an important missing piece. In addition, we believe there is no open tool or framework available to augment clean operational data to product data as it gets generated, adding rich context and experience to the Data Lake.
THE VALUE PROPOSITION
We propose a unified and open-ended industry ready tool/framework to fill this vacuum. The tool can extend a core value proposition around data aggregation, analysis, and insight generation throughout ASIC design & verification workflows without requiring change in existing tools and methodologies. The key value it shall bring includes:
Figure 1. Real-time data aggregation, classification, labeling, and linking across the engineering workflow with ready to use/customizable adapters.
Figure 1 - Real-time data aggregation
Figure 2. Unified Data Lake creation accommodating product (batch) and operational (continuous) data for large multi-site projects.
Figure 2 - Unified Data Lake creation
Figure 3. Building and aggregating a metadata model encompassing hundreds of formats from tens of tools across design and verification workflows and providing ready to use data API for rapid visualization.
Figure 3 - Building and aggregating a metadata model
Figure 4. Advance Analytics built on Data Lake APIs, including Failure Diagnostic Analytics and RTL release prediction amongst others.
Figure 4 - Advanced analytics built on Data Lake APIs
EXISTING TOOLS & SOLUTIONS
There are a few available operational tools in the industry which assist in capturing data from requirement to regression. All these tools revolve around their respective platforms. These tools are unique in their own sense, mostly focusing on spec/test-plan annotation and being able to track completeness with it, also providing detailed analysis and trend reports. Even with these tools, the verification process has multiple data silos and misses the opportunity to have a Unified Data Lake.
The true value of an open-ended and unified and enriched data can create tremendous business opportunities including:
- Improved Operational Transparency: View the real-time updates on operation(s) from multiple data points and make intelligent data driven decisions.
- Data Monetization: Leverage insights from multi-fold data to drive down cost and time-to-market. Use data platform to perform real-time analytics and build ML based applications.
- Data Centered Capability Building: Build right analytics skills within chip design and verification team for data mining. Create new domain specific analytic application on parsed Data API.
NEED OF AN OPEN DATA MANAGEMENT TOOL
The New Tool has to be non-intrusive, have scalable data aggregation, and management functions developed specifically for the Semiconductor Industry. It can be a web-based platform allowing extraction of a variety of data, without requiring any changes in existing chip development processes and tools. The core engine of the New Tool has to be made up of an advance metadata parser which extracts & makes insight available as an API, without the need to transform the original data. The engine also enriches product metadata with operational data in real-time as the project progresses through various stages and functional groups. The platform should allow content to be classified and labeled for easy loading and retrieval of information through various visualization and analytic applications. All these great features can enable ML applications by developing the following core components in Table 1.
Table 1 - New Tool core components
Note: For (6),(7),(8) in the table above: The open system shall allow more plug-in creation and integration including JIRA, Jenkins, Slack, Splunk, etc., further enhancing and personalizing the tool.
The need for an open data management tool is observed, which can work along with different families of tools, can be configured as per custom practices of various organizations, and can accommodate operational data integration in real-time. The New Tool that fulfills all these needs along with providing scalable data API to be consumed by various analytics and machine learning applications.
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