Fusion Compiler Parametric Scenario Extension: Scaling Design Scenarios Without Scaling Risk

Modern SoC designs are increasingly defined by the need to operate across a wide range of use conditions. Mobile applications, in particular, must support both high-performance modes and aggressively optimized low-power states, often within the same design. Beyond mobile, automotive designs add further complexity, with additional considerations such as aging, long-term reliability, and stringent safety requirements. Each of these requirements multiplies the number of design scenarios that must be analyzed and closed, turning scenario coverage into a first-order challenge rather than a downstream concern.

Traditionally, Design Engineers address this complexity by curating a small, fixed subset of so-called “dominant” scenarios and optimizing against them. While this approach favors runtime and capacity, it introduces a fundamental risk: identifying the right dominant scenarios early in a new design is difficult, and often incomplete. When important scenarios are missed, violations remain hidden until late-stage signoff, where they can explode into thousands—or even tens of thousands—of issues. At that point, teams are forced into costly, iterative ECO loops, struggling to converge timing, power, and DRC across scenarios that were never visible during optimization.

To break this cycle, Fusion Compiler’s patent-pending PSX introduces a fundamentally new optimization paradigm within Fusion Compiler. Instead of treating scenarios as a fixed list that must be manually curated, PSX treats scenarios as parametric dimensions of optimization. This shift allows hundreds of signoff scenarios to influence implementation decisions directly and efficiently, without requiring them to be explicitly enabled or individually optimized. 

At the core of PSX is Adaptive Scenario Compression, [Technical Blog, Synopsys.com, Oct 2025] which makes large scenario sets computationally tractable. Compression alone, however, is not the end goal. PSX uses these compressed representations to expose scenario behavior—not raw scenario count—as inputs to optimization. As a result, Fusion Compiler can optimize across the full scenario space, rather than being constrained to a narrow and potentially misleading subset.

This parametric representation allows hundreds of scenarios to influence implementation simultaneously, without requiring each one to be individually optimized. Instead of scaling optimization effort with scenario count, PSX scales insight—making the behavior of the full scenario space visible to the optimizer. 

This approach shifts the optimization question from “Which scenarios should we include?” to “How should optimization respond across the entire scenario space?” By focusing on scenario sensitivity instead of raw scenario count, PSX avoids the pitfalls of missing critical corners while maintaining efficient capacity and runtime behavior. 

By transforming scenario handling from selection‑based to parametric, PSX elevates scenarios from a late‑stage verification burden to a continuous source of optimization intelligence. Instead of over‑optimizing for a few dominant corners and discovering problems late, design teams gain full scenario awareness during implementation—allowing Fusion Compiler to prevent violations rather than chase them downstream.

The result is an implementation flow that continuously balances tradeoffs across objectives, rather than overfitting the design to a few hand-picked corners.

In summary, PSX transforms how scenarios are handled in physical implementation. By turning hundreds of PrimeTime signoff scenarios into parametric optimization intelligence, it allows Fusion Compiler to prevent problems instead of chasing them. As design teams face ever-growing scenario counts driven by power modes, operating conditions, and reliability constraints, this shift from selection-based to parametric optimization becomes essential for predictable, scalable design closure.  

Intel demonstrated that adopting PSX fundamentally transformed block-level timing closure from a late-stage, reactive ECO exercise into a predictable, implementation-native process, enabling full signoff intent—across corners, modes, and voltages—to be exercised early and locally within the block without disrupting upstream construction flows, significantly reducing violation load, stabilizing convergence, and improving overall signoff confidence in production designs.

Shift Left, Block Level, Signoff Optimization, with Corner Convergence, Intel, Silicon Valley SNUG 2026