EPIC: Abstraction and Polymorphism of In-Network Collectives on Ethernet

In-Network Collective (INC) acceleration holds immense potential for optimizing AI training and inference; however, its cross-layer nature has historically hindered investment and adoption within the open Ethernet ecosystem. To bridge this gap, we propose EPIC (Ethernet Polymorphic In-network Collectives), an INC protocol specification and reference system built on the principle of "Unified Abstraction, Polymorphic Realization. " EPIC introduces an abstraction compatible with standard Ethernet that aligns functional boundaries with participant roles, while offering polymorphic realizations tailored to varying hardware capabilities.

We address three fundamental challenges: first, we employ a modular design that enables an evolutionary path from simple to complex implementations, allowing vendors to iterate their hardware incrementally; second, we apply formal verification methodologies to prove the correctness of all proposed polymorphic modes; and third, we develop a unified resource management model versatile enough for diverse INC scenarios. We conduct extensive experiments-model checking, packet/flow simulations, VM emulation, Tofino/NP Testbed, and FPGA/RTL verification-to validate EPIC's correctness, performance gain, and feasibility.