NeuroScalar: A Deep Learning Framework for Fast, Accurate, and In-the-Wild Cycle-Level Performance Prediction

• URL: http://arxiv.org/abs/2509.22410v2
• Date: Mon, 29 Sep 2025 22:23:10 GMT
• Title: NeuroScalar: A Deep Learning Framework for Fast, Accurate, and In-the-Wild Cycle-Level Performance Prediction
• Authors: Shayne Wadle, Yanxin Zhang, Vikas Singh, Karthikeyan Sankaralingam,
• Abstract summary: This paper introduces a novel deep learning framework for high-fidelity, in-the-wild'' simulation on production hardware.<n>Our core contribution is a DL model trained on microarchitecture-independent features to predict cycle-level performance for hypothetical processor designs.<n>We demonstrate that this framework enables accurate performance analysis and large-scale hardware A/B testing on a massive scale.
• Score: 18.863968099669364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The evaluation of new microprocessor designs is constrained by slow, cycle-accurate simulators that rely on unrepresentative benchmark traces. This paper introduces a novel deep learning framework for high-fidelity, ``in-the-wild'' simulation on production hardware. Our core contribution is a DL model trained on microarchitecture-independent features to predict cycle-level performance for hypothetical processor designs. This unique approach allows the model to be deployed on existing silicon to evaluate future hardware. We propose a complete system featuring a lightweight hardware trace collector and a principled sampling strategy to minimize user impact. This system achieves a simulation speed of 5 MIPS on a commodity GPU, imposing a mere 0.1% performance overhead. Furthermore, our co-designed Neutrino on-chip accelerator improves performance by 85x over the GPU. We demonstrate that this framework enables accurate performance analysis and large-scale hardware A/B testing on a massive scale using real-world applications.

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