Abstract
Recent research on vision backbone architectures has predominantly focused on optimizing efficiency for hardware platforms with high parallel processing capabilities. This category increasingly includes embedded systems such as mobile phones and embedded AI accelerator modules. In contrast, CPUs do not have the possibility to parallelize operations in the same manner, wherefore models benefit from a specific design philosophy that balances amount of operations (MACs) and hardware-efficient execution by having high MACs per second (MACpS). In pursuit of this, we investigate two modifications to standard convolutions, aimed at reducing computational cost: grouping convolutions and reducing kernel sizes. While both adaptations substantially decrease the total number of MACs required for inference, sustaining low latency necessitates preserving hardware-efficiency. Our experiments across diverse CPU devices confirm that these adaptations successfully retain high hardware-efficiency on CPUs. Based on these insights, we introduce CPUBone, a new family of vision backbone models optimized for CPU-based inference. CPUBone achieves state-of-the-art Speed-Accuracy Trade-offs (SATs) across a wide range of CPU devices and effectively transfers its efficiency to downstream tasks such as object detection and semantic segmentation. Models and code are available at this https URL.
Abstract (translated)
近期关于视觉骨干架构的研究主要集中于优化适用于高并行处理硬件平台的效率，此类平台日益包括移动设备和嵌入式AI加速模块等嵌入式系统。相比之下，CPU无法以相同方式实现操作并行化，因此模型需采用特定的设计理念——通过高单位时间乘累加操作数（MACpS）来平衡运算总量（MACs）与硬件高效执行。为实现这一目标，我们研究了两种针对标准卷积的改进方案，旨在降低计算成本：分组卷积与减小核尺寸。尽管这两种调整均显著减少了推理所需的MACs总量，但维持低延迟仍需保持硬件效率。我们在多种CPU设备上的实验证实，这些调整能成功在CPU上保持高硬件效率。基于这些发现，我们提出了CPUBone——一个专为CPU推理优化的新型视觉骨干模型家族。CPUBone在各类CPU设备上实现了先进的速度-精度权衡（SAT），并能将效率有效迁移至目标检测、语义分割等下游任务。模型与代码已公开于此链接。
URL
https://arxiv.org/abs/2603.26425
