标题： 在边缘实现混合视觉变换器（ViT）网络的高效硬件加速 显示英文标题

标题： Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge

摘要： 混合视觉变压器结合传统神经网络（NN）和视觉变压器（ViT）的元素，以实现轻量级和精确的检测。 然而，它们在资源受限的边缘设备上的高效部署仍面临一些挑战。 混合模型由于存在广泛多样的NN层类型和大型中间数据张量，阻碍了高效的硬件加速。 为了在边缘执行，本文在硬件-调度堆栈中提出了创新：a.) 在最低级别，一个可配置的PE数组支持所有混合ViT层类型；b.) 在同一层内的时间循环重新排序，使硬件支持归一化和softmax层，最小化片上数据传输；c.) 进一步的调度优化利用反向瓶颈层之间的层融合，大幅减少片外内存传输。 所得到的加速器采用28nm CMOS实现，达到25.6 GMACs/s时的峰值能效1.39 TOPS/W。 显示英文摘要

摘要： Hybrid vision transformers combine the elements of conventional neural networks (NN) and vision transformers (ViT) to enable lightweight and accurate detection. However, several challenges remain for their efficient deployment on resource-constrained edge devices. The hybrid models suffer from a widely diverse set of NN layer types and large intermediate data tensors, hampering efficient hardware acceleration. To enable their execution at the edge, this paper proposes innovations across the hardware-scheduling stack: a.) At the lowest level, a configurable PE array supports all hybrid ViT layer types; b.) temporal loop re-ordering within one layer, enabling hardware support for normalization and softmax layers, minimizing on-chip data transfers; c.) further scheduling optimization employs layer fusion across inverted bottleneck layers to drastically reduce off-chip memory transfers. The resulting accelerator is implemented in 28nm CMOS, achieving a peak energy efficiency of 1.39 TOPS/W at 25.6 GMACs/s.