Linear-Time Non-Local Codec Placement for Machine Learning Model Compression

Abstract:On-device inference imposes constraints on model storage and operand traffic and thus benefits from compressed operand representations, which mandate the use of encoding and decoding operators. However, na\"ıve placement of codec operators can cancel the operand traffic gains sought from compression. In this work, I present an algorithm to place codec operators into a source computational graph to make it compatible with target hardware. The edges of the graph are colored so that each induced subgraph is weakly connected and has a maximal node that both postdominates all subgraph inputs and dominates all subgraph outputs; this unique node - dubbed the $\textit{absolute dominator}$ - is the natural location to observe the traffic flowing through the subgraph and learn parametric codec operators. The algorithm's time complexity is linear in the source graph's size. With respect to na\"ıve local codec placement, the algorithm reduces modeled operand traffic by $24\%$-$44\%$ across four models, including $24\%$ on SigLIP and $34\%$ on SmolLM2.

Machine Learning (cs.LG)

arXiv:2610.14703 [cs.LG]

(or arXiv:2610.14703v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.14703 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.