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New framework ProCerDCL verifies deep learning compiler optimizations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ProCerDCL, a new framework designed for efficient black-box probabilistic verification of deep learning compiler optimizations. This tool assesses the probability of a property violation against a specified threshold, providing an acceptance, rejection, or inconclusive verdict with bounded error probabilities. ProCerDCL utilizes a sequential test that is more sample-efficient than existing methods and has been applied to verify semantic consistency between reference and compiled executions in TorchInductor and TensorRT, successfully identifying compiler faults. AI

IMPACT Enhances the reliability and efficiency of deep learning model deployment by improving compiler verification processes.

RANK_REASON The cluster contains a research paper detailing a new framework for verifying deep learning compiler optimizations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework ProCerDCL verifies deep learning compiler optimizations

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14 / 100
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The cluster contains a research paper detailing a new framework for verifying deep learning compiler optimizations. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zexin Li, Weibo Zhang, Linyi Li, Arvind Easwaran, Simin Chen ·

    ProCerDCL: Query-Efficient Black-Box Probabilistic Verification of Deep Learning Compiler Optimization

    arXiv:2610.36499v1 Announce Type: cross Abstract: We present \tool, a query-efficient framework for black-box probabilistic verification. Given a fixed, sampleable input distribution and a binary property oracle, \tool assesses the property's violation probability $p$ against a t…

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