Architectures that incorporate Computing-in-Memory (CiM) using emerging
non-volatile memory (NVM) devices have become strong contenders for deep neural
network (DNN) acceleration due to their impressive energy efficiency. Yet, a
significant challenge arises when using these emerging devices: they can show
substantial variations during the weight-mapping process. This can severely
impact DNN accuracy if not mitigated. A widely accepted remedy for imperfect
weight mapping is the iterative write-verify approach, which involves verifying
conductance values and adjusting devices if needed. In all existing
publications, this procedure is applied to every individual device, resulting
in a significant programming time overhead. In our research, we illustrate that
only a small fraction of weights need this write-verify treatment for the
corresponding devices and the DNN accuracy can be preserved, yielding a notable
programming acceleration. Building on this, we introduce USWIM, a novel method
based on the second derivative. It leverages a single iteration of forward and
backpropagation to pinpoint the weights demanding write-verify. Through
extensive tests on diverse DNN designs and datasets, USWIM manifests up to a
10x programming acceleration against the traditional exhaustive write-verify
method, all while maintaining a similar accuracy level. Furthermore, compared
to our earlier SWIM technique, USWIM excels, showing a 7x speedup when dealing
with devices exhibiting non-uniform variations.

利用新兴的非易失性存储器设备进行计算存储的架构在深度神经网络加速中具有出色的能效，然而，这些新兴设备在权重映射过程中存在重大变化的挑战，通过我们的研究，我们引入了一种基于二阶导数的新方法 USWIM，可以加速编程达到 10 倍，同时保持类似的准确性。

U-SWIM: 通用选择性写入验证用于计算内存神经加速器

U-SWIM: Universal Selective Write-Verify for Computing-in-Memory Neural  Accelerators

Medical imaging has witnessed remarkable progress but usually requires a
large amount of high-quality annotated data which is time-consuming and costly
to obtain. To alleviate this burden, semi-supervised learning has garnered
attention as a potential solution. In this paper, we present Meta-Learning for
Bootstrapping Medical Image Segmentation (MLB-Seg), a novel method for tackling
the challenge of semi-supervised medical image segmentation. Specifically, our
approach first involves training a segmentation model on a small set of clean
labeled images to generate initial labels for unlabeled data. To further
optimize this bootstrapping process, we introduce a per-pixel weight mapping
system that dynamically assigns weights to both the initialized labels and the
model's own predictions. These weights are determined using a meta-process that
prioritizes pixels with loss gradient directions closer to those of clean data,
which is based on a small set of precisely annotated images. To facilitate the
meta-learning process, we additionally introduce a consistency-based Pseudo
Label Enhancement (PLE) scheme that improves the quality of the model's own
predictions by ensembling predictions from various augmented versions of the
same input. In order to improve the quality of the weight maps obtained through
multiple augmentations of a single input, we introduce a mean teacher into the
PLE scheme. This method helps to reduce noise in the weight maps and stabilize
its generation process. Our extensive experimental results on public atrial and
prostate segmentation datasets demonstrate that our proposed method achieves
state-of-the-art results under semi-supervision. Our code is available at
this https URL

本研究提出了一种用于半监督医学图像分割的元学习方法，通过先使用少量干净标记图像进行训练来生成未标记数据的初始标签，并引入基于损失梯度方向的像素权重映射系统来进一步优化引导过程。此外，还引入了一种一致性伪标签增强方案来提高模型自身预测的质量，并通过引入平均教师来减少噪声并稳定权重映射的生成过程。实验结果表明，所提出的方法在半监督任务下取得了最先进的结果。