QBitOpt：训练期间快速且准确的位宽重分配
QBitOpt: Fast and Accurate Bitwidth Reallocation during Training
AI总结：
QBitOpt 将位宽分配建模为约束优化问题，在量化感知训练中结合快速敏感度计算与高效求解器，生成保证满足资源约束的高性能混合精度网络，并在 ImageNet 上优于现有方法。
摘要：
量化神经网络是在移动和嵌入式设备上实现高效推理的最有效方法之一。特别是混合精度量化（MPQ）网络，其各层可以被量化到不同的位宽，在相同资源约束下相比具有同质位宽的网络能够获得更好的任务性能。然而，寻找最优的位宽分配是一个具有挑战性的问题，因为搜索空间随着网络层数的增加呈指数增长。在本文中，我们提出了 QBitOpt，一种在量化感知训练（QAT）期间更新位宽的新算法。我们将位宽分配问题形式化为一个约束优化问题。通过在 QAT 期间将快速计算的敏感度与高效求解器相结合，QBitOpt 能够生成具有高任务性能的混合精度网络，并保证满足严格的资源约束。这与现有的使用梯度学习位宽且无法提供此类保证的混合精度方法形成对比。我们在 ImageNet 上评估了 QBitOpt，并确认在文献中常见的平均位宽约束下，我们优于现有的固定精度和混合精度方法。
英文摘要：
Quantizing neural networks is one of the most effective methods for achieving efficient inference on mobile and embedded devices. In particular, mixed precision quantized (MPQ) networks, whose layers can be quantized to different bitwidths, achieve better task performance for the same resource constraint compared to networks with homogeneous bitwidths. However, finding the optimal bitwidth allocation is a challenging problem as the search space grows exponentially with the number of layers in the network. In this paper, we propose QBitOpt, a novel algorithm for updating bitwidths during quantization-aware training (QAT). We formulate the bitwidth allocation problem as a constraint optimization problem. By combining fast-to-compute sensitivities with efficient solvers during QAT, QBitOpt can produce mixed-precision networks with high task performance guaranteed to satisfy strict resource constraints. This contrasts with existing mixed-precision methods that learn bitwidths using gradients and cannot provide such guarantees. We evaluate QBitOpt on ImageNet and confirm that we outperform existing fixed and mixed-precision methods under average bitwidth constraints commonly found in the literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
