CrossNAS自动化优化机器学习在存算一体系统中的部署
CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems
- 采用单路径一次性权重共享与进化搜索结合策略
- 在精度和能效上超越现有方法，搜索时间相当或更短
- 适合存算一体系统设计者与芯片架构研究者
本文提出CrossNAS框架，一种自动化方法，用于探索涵盖电路、架构和系统多层抽象的广阔设计空间，以优化机器学习工作负载在模拟存算一体（PIM）系统上的部署。CrossNAS首次在PIM系统映射与优化中结合单路径一次性权重共享策略与进化搜索。该框架在准确率和能效方面均树立了新的基准，相比之前方法在性能上取得提升，同时保持相当或更短的搜索时间。
原文摘要 · Abstract (English)
In this paper, we propose the CrossNAS framework, an automated approach for exploring a vast, multidimensional search space that spans various design abstraction layers-circuits, architecture, and systems-to optimize the deployment of machine learning workloads on analog processing-in-memory (PIM) systems. CrossNAS leverages the single-path one-shot weight-sharing strategy combined with the evolutionary search for the first time in the context of PIM system mapping and optimization. CrossNAS sets a new benchmark for PIM neural architecture search (NAS), outperforming previous methods in both accuracy and energy efficiency while maintaining comparable or shorter search times.
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