这篇论文提出了ThRIve方法，用低秩适应应对PIM芯片的热噪声问题，在保持精度不变的前提下把能效提升了5.4倍，做硬件推理的值得看看。
ThRIve是一种针对非易失性存储器PIM架构的热噪声感知训练方法，通过低秩适应（LoRA）选择性将低秩噪声感知参数存储在抗热性更强的硬件上。实验表明，该方法使CNN推理在整个工作温度范围内的平均精度保持在理想（无噪声）精度的2%以内，精度波动也在平均值的2%以内。与热鲁棒的SRAM-based PIM系统相比，ThRIve在保持相当精度和鲁棒性的同时，实现了高达5.4倍的能效延迟积（EDP）降低。
ThRIve: Thermally Robust CNN Inference via Low-Rank Adaptation in Heterogeneous PIM Architectures
Processing-In-Memory (PIM) has emerged as a promising technology for accelerating machine learning (ML) workloads. Specifically, non-volatile memory-based PIM architectures have enabled effective ML acceleration due to their ability to perform energy-efficient matrix-vector multiplication operations. However, these devices suffer from non-idealities such as thermal noise. This noise alters the stored values in the memory cells which correspond to actual model weights, compromising the inference accuracy. In this work, we introduce ThRIve, a noise-aware training methodology that leverages low-rank adaptation to enable thermally robust inference on heterogeneous PIM architectures. ThRIve selectively stores these low-rank noise-aware parameters on a hardware that is less susceptible to thermal noise, enabling robustness against temperature-induced noise variations. ThRIve mitigates the effects of thermal-noise and prevent the drop in inference accuracy across the entire operating temperature range. Experimental results demonstrate that ThRIve-enabled architectures maintain consistent inference accuracy, with the mean accuracy staying within 2% of the ideal (i.e., noise-free) accuracy, and the variation in accuracy across the entire operating temperature range remaining within 2% of the mean. The proposed methodology achieves accuracy and robustness comparable to thermally-resilient Static Random-Access Memory (SRAM)-based PIM systems, while delivering up to 5.4x reduction in energy-delay product (EDP) during CNN model inferencing.