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A Novel Approach to Reducing Testing Costs and Minimizing Defect Escapes Using Dynamic Neighborhood Range and Shapley Values
Hardware Review Research

A Novel Approach to Reducing Testing Costs and Minimizing Defect Escapes Using Dynamic Neighborhood Range and Shapley Values

Admin • (Updated: ) • 2 min read

Abstract

This article presents a novel verification methodology aimed at substantially reducing hardware testing costs while simultaneously minimizing defect escapes. The approach integrates Dynamic Neighborhood Range (DNR) algorithms for efficient adaptive test generation with Shapley Values (SV) for quantitative test contribution analysis and prioritization. By leveraging SV to objectively measure the marginal utility of test vectors to coverage, the framework optimizes test suite selection, promising enhanced reliability and significant resource savings in electronic design automation.

Report

Analysis Report: Reducing Testing Costs via DNR and Shapley Values

Key Highlights

  • Hybrid Optimization Framework: The paper introduces a synergistic combination of machine learning fairness metrics (Shapley Values) and adaptive search techniques (Dynamic Neighborhood Range) applied to hardware verification.
  • Quantifiable Test Utility: Shapley Values are utilized to provide a mathematically rigorous assessment of the value or contribution of individual test cases or architectural features to achieving complete verification coverage.
  • Efficiency and Quality Gain: The core innovation is achieving the dual goal of reducing the overall test execution time (lower costs) without compromising the thoroughness required to prevent defect escapes (higher quality).
  • Target Domain: The methodology is specifically relevant for complex, high-dimensional design spaces, such as those found in modern processor cores and System-on-Chips (SoCs).

Technical Details

  • Dynamic Neighborhood Range (DNR): This adaptive search or sampling technique is employed to dynamically adjust the focus of test generation. DNR likely focuses exploration on regions of the design space that are known to be sparse, prone to defects, or previously underserved by static test suites, ensuring efficient coverage maximization.
  • Shapley Values (SV) Application: Borrowed from cooperative game theory, Shapley Values are used post-simulation. If a test suite is considered the "game," and coverage is the "payout," SV determines the fair contribution of each test vector to the total coverage achieved. This allows for precise identification and removal of redundant tests that contribute little marginal benefit, leading to optimal test suite reduction.
  • Methodology Integration: The DNR likely feeds into the test environment to generate targeted test inputs, and the resulting coverage data is then processed by the Shapley framework to refine future DNR targets and prune the final test set.

Implications for the RISC-V/Tech Ecosystem

  • Verification Bottleneck Solution: As the RISC-V instruction set architecture (ISA) gains complexity through custom extensions and highly customized implementations, verification becomes the dominant cost driver. This approach directly addresses the verification bottleneck, enabling faster time-to-market for specialized RISC-V cores.
  • Enhanced Customization Confidence: One of RISC-V's strengths is flexibility. However, every customization requires extensive re-verification. By lowering the cost and increasing the confidence in defect detection during verification, this method encourages more rapid and robust development of proprietary RISC-V extensions (e.g., custom vector units or security features).
  • Scalability for Formal/Simulation Integration: The quantitative nature of Shapley Values provides a clear metric for resource allocation, which is crucial for hybrid verification environments that combine formal methods, simulation, and emulation. This optimization improves the feasibility of verifying large, multi-core RISC-V architectures.
  • Higher Reliability for Critical Applications: Minimizing defect escapes is paramount for RISC-V deployments in safety-critical domains (e.g., automotive or industrial control). This methodology provides a verifiable, data-driven mechanism to assure high quality, fostering trust in the overall ecosystem.