LowFormer: Hardware Efficient Design for Convolutional Transformer Backbones

TL;DR

LowFormer is a new convolutional transformer backbone that emphasizes real-world efficiency by optimizing throughput and latency, achieving faster performance with comparable or better accuracy across various hardware platforms.

Contribution

The paper introduces LowFormer, a hardware-efficient backbone design that combines macro and micro architectural optimizations, including a slimmed-down MultiHead Self-Attention, validated across multiple hardware types.

Findings

LowFormer significantly improves throughput and latency.

It maintains or surpasses state-of-the-art accuracy.

Effective for object detection and semantic segmentation.

Abstract

Research in efficient vision backbones is evolving into models that are a mixture of convolutions and transformer blocks. A smart combination of both, architecture-wise and component-wise is mandatory to excel in the speedaccuracy trade-off. Most publications focus on maximizing accuracy and utilize MACs (multiply accumulate operations) as an efficiency metric. The latter however often do not measure accurately how fast a model actually is due to factors like memory access cost and degree of parallelism. We analyzed common modules and architectural design choices for backbones not in terms of MACs, but rather in actual throughput and latency, as the combination of the latter two is a better representation of the efficiency of models in real applications. We applied the conclusions taken from that analysis to create a recipe for increasing hardware-efficiency in macro design. Additionally we introduce a simple slimmed-down version of MultiHead Self-Attention, that aligns with our analysis. We combine both macro and micro design to create a new family of hardware-efficient backbone networks called LowFormer. LowFormer achieves a remarkable speedup in terms of throughput and latency, while achieving similar or better accuracy than current state-of-the-art efficient backbones. In order to prove the generalizability of our hardware-efficient design, we evaluate our method on GPU, mobile GPU and ARM CPU. We further show that the downstream tasks object detection and semantic segmentation profit from our hardware-efficient architecture. Code and models are available at https://github.com/ altair199797/LowFormer.