AI 中文总结
DiffPower是GPU加速的可微分功耗分析框架，通过转换网表为字节码实现高加速，功耗梯度计算高效，可用于单元尺寸调整和功耗病毒生成，性能优于传统方法。
AI 中文摘要
精确且可扩展的开关功耗分析仍是现代物理设计中的关键瓶颈，常迫使人在计算速度与建模保真度间做权衡。我们提出DiffPower，一种用于可微分功耗分析与优化的GPU加速框架。DiffPower将设计网表转换为与PDK无关的字节码表示，通过反向模式自动微分实现解析梯度计算，在评估的最大设计上，相较于单线程CPU传播实现了最高1002倍的加速，且GPU优势随设计规模增大而提升。融合解析建模与并行仿真的混合传播方法，在10个工业及基准设计上实现了中位数翻转率相关系数r=0.96。所得的功耗梯度，相较于CPU有限差分方法计算速度最高提升904倍且秩一致性近乎完美，可支持两项下游应用：（1）梯度加权单元尺寸调整，在工业设计上较局部功耗启发式方法实现最高2.98倍的改进，在11.7万单元规模下，竞争方法已达平台期，其优势更为显著；（2）通过梯度上升生成功耗病毒，可实现最高2.13倍的过渡加权功耗，替代了传统需耗时数小时的搜索过程。
英文摘要
Accurate and scalable switching power analysis remains a critical bottleneck in modern physical design, often forcing a trade-off between computational speed and modeling fidelity. We present DiffPower, a GPU-accelerated framework for differentiable power analysis and optimization. DiffPower translates design netlists into a PDK-agnostic bytecode representation, enabling analytical gradient computation via reverse-mode automatic differentiation, achieving up to a $1{,}002\times$ speedup over single-threaded CPU propagation on the largest evaluated design, with the GPU advantage growing with design scale. A hybrid propagation methodology fusing analytical modeling with parallel simulation achieves a median toggle-rate correlation of $r{=}0.96$ across ten industrial and benchmark designs. The resulting \emph{power gradients}, computed up to $904\times$ faster than CPU finite-difference methods with near-perfect rank agreement, enable two downstream applications: (1) gradient-weighted cell sizing, which achieves up to $2.98\times$ improvement over local-power heuristics on industrial designs, with even stronger advantages at the 117K-cell scale where competing methods plateau; and (2) power virus generation via gradient ascent, which yields up to $2.13\times$ higher transition-weighted power, replacing a search process that traditionally requires hours.
