System for intelligent time termination in hierarchical system-on-chip (SoC)

Abstract

A system for intelligent timing closure in hierarchical system-on-chip (SoC), comprising: a) a constraint learning and propagation module configured to analyze timing constraints using machine learning and propagate refined constraints across design partitions; b) a cross-boundary timing path analyzer configured to detect and analyze timing paths spanning multiple design modules; c) a predictive timing violation detector configured to predict potential timing violations based on historical and real-time design data; d) an intelligent optimization recommendation system configured to generate context-dependent timing optimization suggestions; and e) a timing closure progress tracker and feedback loop configured to monitor timing closure progress and refine system behavior through adaptive feedback; f) the system collaboratively enables efficient timing convergence in hierarchical SoC environments.

Description

The present invention relates to a system that provides a smart framework for achieving timing closure in hierarchical system-on-a-chip (SoC) designs by analyzing and optimizing timing paths across multiple design partitions. It uses machine learning algorithms and constraint-controlled methods to dynamically address timing bottleneck and reduce iteration cycles.Achieving closed timing in hierarchical system-on-a-chip (SoC) designs has become increasingly complex due to the increasing size, integration density, and functionality of modern chips. Conventional timing analysis techniques often have difficulty coping with dependencies between multiple IP blocks, subsystems, and clock domains, resulting in extended design cycles and multiple costly iterations. These challenges are further compounded by the limited visibility across design partitions, making global timing optimization a difficult and error prone process.Existing tools and methods are generally based on manual tuning, static constraint propagation and trial and error approaches in order to achieve closed timing. However, these methods are inefficient in hierarchical environments where timing paths often span multiple modules and abstraction levels. The lack of smart instructions and adaptive feedback mechanisms limits designers' opportunities to proactively solve timing problems, often resulting in late phase experiences and performance target misses.To overcome these limitations, the invention provides a smart timing closure system that keeps track of a problem solution approach with predictive analytics, real-time feedback, and hierarchical awareness. By dynamically analyzing timing data, learning from historical closure patterns, and applying optimization strategies beyond design limits, the system provides a more intelligent, faster, and accurate way of timing convergence. This approach significantly increases productivity and ensures robust timing performance in complex SoC designs.An object of the present disclosure is that the system significantly reduces timing closure cycles by automatizing data acquisition, constraint refinement, and optimization across hierarchical SoC designs.Another object of the present disclosure is to enable consistent visibility of timing paths across multiple IP blocks and abstraction layers, allowing more accurate and comprehensive timing analysis.Another object of the present disclosure is to predict timing violations before they occur and the system allows for early intervention, thereby reducing last minute experiences and costly design revisions.Another object of the present disclosure is to dynamically adjust and pass timing constraints in a contextual manner, thereby eliminating inconsistencies between design partitions.Another object of the present invention is to intelligently recommend targeted timing corrections based on historical patterns and real-time design analyses, thus minimizing trial and error overhead.The feedback loop will continue to develop over time and improve prediction accuracy and quality of optimization with each design iteration.Another object of the present disclosure is to integrate the modules into EDA tools that are customary in industry, thereby ensuring compatibility and simple takeover into current design workflows.The present invention relates to a system for achieving intelligent timing termination in hierarchical SoC. It integrates data-driven decision making with advanced analysis tools to rationalize the locking efforts in complex architectures.Another embodiment of the present invention is that the system collects timing data from various levels of the SoC hierarchy, including leaf level IPs and top level interconnects. This consistent view allows for accurate path analysis across partitions.Another embodiment of the present invention is a dedicated module that uses historical data and machine learning to refine and pass timing constraints. This adaptive approach replaces manual tuning with intelligent, automated constraint management.Another embodiment of the present invention is that the specialized algorithms identify and evaluate timing paths that cross multiple modules.Another embodiment of the present invention is that the AI-controlled models predict future timing violations based on design evolution and allow for early intervention. This proactive ability significantly reduces costly late phase repair.In another embodiment of the present invention, the system recommends optimization strategies tailored to each timing problem, such as rearranging logic or inserting buffers. Another embodiment of the present invention is that the feedback mechanism tracks the effectiveness of the optimizations applied and the progress of timing. Another embodiment of the present invention is that by automatizing analysis, prediction and optimization, the system speeds up completion of timing and shortens design cycles.The present invention relates to a system for intelligent management of timing closure in hierarchical system-on-a-chip (SoC) designs. It includes six key modules: the hierarchical timing data aggregation module collects and integrates timing data from different SoC levels; the constraint learning and propagation engine refines timing constraints using machine learning; the cross-boundary timing path analyzer identifies and analyzes paths across design partitions; the predictive timing visualization detector predicts potential timing issues before they occur; the intelligent optimization recommendation system proposes targeted corrections; and the timing closure progress tracker and feedback loop monitors the locking status and continuously improves the system by adaptive learning.Hierarchical Module for Aggregating Timing DataThis module is responsible for collecting and aggregating timing data at all levels of the SoC hierarchy, including leaf IP blocks, intermediate subsystems, and top level integration. It supports standardized data formats and interfaces to EDA tools to extract static timing analysis (STA) reports, constraint files, and netlist information. The module intelligently correlates timing paths that span multiple partitions and clock domains and ensures that no critical path is missed. It also performs dependency mapping to understand how local timing violations may affect global timing targets, thus forming the basis for effective cross-hierarchy analysis.Module for Constraint Learning and PropagationThis module employs machine learning techniques and rule-based algorithms to understand and refine timing constraints throughout the design hierarchy. By analyzing historical timing closure data and current timing jitter distributions, sub- or congested regions may be identified and optimal timing margins recommended. It also automates the passing of constraints between modules and ensures that they are aligned and context dependent. This significantly reduces the manual effort and improves the consistency and quality of the constraints across hierarchical boundaries, which enables a more rapid convergence.Cross-Boundary Timing Path AnalyzerThe cross-boundary timing path analyzer is specialized for identifying and analyzing timing paths traversing various partitions or hierarchy levels. It tracks these paths from the source to the sink, regardless of how deep or wide they extend across the SoC. The analyzer evaluates each segment of the path using localized models and then combines the results to generate a global perspective for timing violations. It takes into account clock skew, latency variations, and boundary delays arising from hierarchical integration, thus allowing precise identification of the true cause of timing issues.Predictive Timing Violation DetectorThis module uses predictive analyses and AI models trained on previous SoC design data to predict potential timing violations before they occur. By monitoring design evolution, RTL changes, and timing trends, it can proactively identify paths or blocks that are likely to result in closing delays. Moreover, confidence levels and severity levels are specified for each prediction so that designers can prioritize their interventions. This early warning function helps avoid late phase experiences, reduce revision, and allow more informed design decisions throughout the project life cycle.Intelligent System for Optimization RecommendationsOnce timing bottleneckes are identified, this system generates targeted optimization suggestions. These may include the insertion of buffers, the redesign of logic, the matching of clock trees, or the matching of constraints. The recommendations are context dependent, i.e., they take into account the hierarchical location, the effects on the timing, and the design constraints of the paths involved. The system arranges the options according to their predicted effectiveness and implementation complexity and gives the designers a clear travel plan for efficient locking. It also supports simulation-in-the-loop to validate the effects of the proposed optimizations in near real-time.Progress Tracking and Feedback Loop for Schedule CompletionTo ensure continuous improvement and adaptive learning, this module tracks the progress of the time termination over several iterations. It records which optimizations were applied, their results, and all remaining violations. This data enters the system's learning models to improve future predictions and recommendations. The module also provides dashboards and real-time reports that allow the project teams to comprehensively view the completion status throughout the hierarchy. This allows for better planning, responsibility, and joint debugging during complex SoC developments.Operation of the SystemThe operation of the intelligent timing termination system in hierarchical system-on-a-chip (SoC) designs begins with the hierarchical timing data aggregation module, which extracts and uniformizes timing reports, constraints and netlist data from all levels of the design hierarchy via an interface with industry-standard EDA tools. This aggregated data is then passed to the Constraint Learning and Propagation module where machine learning and rule-based algorithms analyze previous timing closure results and current design conditions to refine timing constraints and transmit them context-dependently across module boundaries. Next, the cross-boundary timing path analyzer performs a detailed check of all timing paths that extend across multiple subsystems or abstraction layers to ensure that delays and offsets between the partitions are properly accounted for. As design progresses, the predictive timing visualization detector continuously monitors design changes and timing metrics and uses AI models to predict potential injuries with associated severity levels so that designers can take corrective action early. When injury is identified, the intelligent optimization recommendation system evaluates a number of correction strategies such as logical redesigns or constraints adjustments, and presents suggestions that are ordered by their anticipated effectiveness and implementation costs. Throughout the process, the timing closure progress tracker and feedback loop records each action, decision, and result and feeds this data back to the learning models of the system to continuously refine the predictions and recommendations. This seamless coordination of modules allows a dynamic, smart, and adaptive timing solution approach that significantly reduces manual effort, design iterations, and the total time until complex SoC products are introduced on the market.

Claims

A system for intelligently closing timings in hierarchical system-on-a-chip (SoC) comprising: a) a constraint learning and propagation module configured to analyze timing constraints using machine learning and propagate refined constraints across design partitions; b) a cross-boundary timing path analyzer configured to recognize and analyze timing paths that extend across multiple design modules; c) a predictive timing visualization detector configured to predict potential timing violations based on historical and real-time design data; d) a smart optimization recommendation system configured to generate contextual timing optimization suggestions; and e) a timing closure progress tracker and a feedback loop configured to monitor timing closure progress and refine system behavior by adaptive feedback; f) the system in cooperation enabling efficient timing convergence in hierarchical SoC environments.The system of claim 1, wherein the hierarchical timing data aggregation module interfaces with static timing analysis tools to extract reports and constraint files in standardized formats.The system of claim 1, wherein the constraint learning and propagation engine uses supervised learning models trained on previous SoC timing Closure projects.The system of claim 1, wherein the cross boundary timing path analyzer accounts for clock skew between modules, latency variations, and boundary delays during path analysis.The system of claim 1, wherein the predictive timing violation detector assigns confidence values to the predicted timing violation based on the severity and the probability of the failure.The system of claim 1, wherein the intelligent optimization recommendation system provides optimization strategies selected from buffer insertion, logical redesign and constraint tuning.The system of claim 1, wherein the timing closure progress tracker and the feedback loop generate visual dashboards to indicate the real-time status of the timing closure progress across hierarchical levels.The system of claim 1, wherein all modules operate in an integrated environment with joint access to design data enabling cooperative and iterative closing cycles.