Crossbar architecture based devices have been widely adopted in neural
network accelerators by taking advantage of the high efficiency on
vector-matrix multiplication (VMM) operations. However, in the case of
convolutional neural networks (CNNs), the efficiency is compromised
dramatically due to the large amounts of data reuse. Although some mapping
methods have been designed to achieve a balance between the execution
throughput and resource overhead, the resource consumption cost is still huge
while maintaining the throughput.
Network pruning is a promising and widely studied leverage to shrink the
model size. Whereas, previous work didn`t consider the crossbar architecture
and the corresponding mapping method, which cannot be directly utilized by
crossbar-based neural network accelerators. Tightly combining the crossbar
structure and its mapping, this paper proposes a crossbar-aware pruning
framework based on a formulated L0-norm constrained optimization problem.
Specifically, we design an L0-norm constrained gradient descent (LGD) with
relaxant probabilistic projection (RPP) to solve this problem. Two grains of
sparsity are successfully achieved: i) intuitive crossbar-grain sparsity and
ii) column-grain sparsity with output recombination, based on which we further
propose an input feature maps (FMs) reorder method to improve the model
accuracy. We evaluate our crossbar-aware pruning framework on median-scale
CIFAR10 dataset and large-scale ImageNet dataset with VGG and ResNet models.
Our method is able to reduce the crossbar overhead by 44%-72% with little
accuracy degradation. This work greatly saves the resource and the related
energy cost, which provides a new co-design solution for mapping CNNs onto
various crossbar devices with significantly higher efficiency.

该论文提出了一种基于 L0-norm 约束优化问题的交叉栏架构感知修剪框架，其中采用了 L0-norm 约束梯度下降（LGD）与弛豫概率投影（RPP）来寻找两个阶段的稀疏性，并提出了输入特征映射（FMs）重新排序方法来提高模型准确性。实验结果表明，该方法可在不降低准确性的情况下将交叉栏架开销减少 44％-72％，为在各种交叉栏架设备上映射 CNN 提供了一种高效的新型协同设计解决方案。