As particle accelerators increase their collision rates, and machine learning solutionsprove their reliability, the need for lightweight and fast neural network hardwareimplementations grows for low-latency tasks such as triggering. We examine thepotential of one recent Lorentz- and permutation-symmetric architecture, PELICAN,and present its instances with as few as 19 trainable parameters that outperformgeneric architectures with tens of thousands of parameters when compared on thebinary classification task of top quark jet tagging.