  There can be numerous electronic components on a given PCB, making the task
of visual inspection to detect defects very time-consuming and prone to error,
especially at scale. There has thus been significant interest in automatic PCB
component detection, particularly leveraging deep learning. However, deep
neural networks typically require high computational resources, possibly
limiting their feasibility in real-world use cases in manufacturing, which
often involve high-volume and high-throughput detection with constrained edge
computing resource availability. As a result of an exploration of efficient
deep neural network architectures for this use case, we introduce PCBDet, an
attention condenser network design that provides state-of-the-art inference
throughput while achieving superior PCB component detection performance
compared to other state-of-the-art efficient architecture designs. Experimental
results show that PCBDet can achieve up to 2$\times$ inference speed-up on an
ARM Cortex A72 processor when compared to an EfficientNet-based design while
achieving $\sim$2-4\% higher mAP on the FICS-PCB benchmark dataset.
