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CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems

arXiv 2505.22868 ↗First posted 28 May 2025cs.ET cs.AR cs.LGcited by 0

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Abstract (arXiv)

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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References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2016An overview of gradient descent optimization algorithmscited by 1,033
  5. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 837
  6. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 673
  7. 2019Single Path One-Shot Neural Architecture Search with Uniform Samplingcited by 414
  8. 2019Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hourscited by 87
  9. 2019PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inferencecited by 61
  10. 2018You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimizationcited by 23
  11. 2021Uncertainty Modeling of Emerging Device-based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Searchcited by 17
  12. 2023AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computingcited by 10
4 more
  1. 2021An In-Memory Analog Computing Co-Processor for Energy-Efficient CNN Inference on Mobile Devicescited by 9
  2. 2022Interconnect Parasitics and Partitioning in Fully-Analog In-Memory Computing Architecturescited by 6
  3. 2023Heterogeneous Integration of In-Memory Analog Computing Architectures with Tensor Processing Unitscited by 4
  4. 2024Multi-Objective Neural Architecture Search for In-Memory Computingcited by 1

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