Deploying expressive learning models directly on programmable dataplanes promises line-rate, low-latency traffic analysis but remains hindered by strict hardware constraints and the need for predictable, auditable behavior. Chimera introduces a principled framework that maps attention-oriented neural computations and symbolic constraints onto dataplane primitives, enabling trustworthy inference within the match-action pipeline. Chimera combines a kernelized, linearized attention approximation with a two-layer key-selection hierarchy and a cascade fusion mechanism that enforces hard symbolic guarantees while preserving neural expressivity. The design includes a hardware-aware mapping protocol and a two-timescale update scheme that together permit stable, line-rate operation under realistic dataplane budgets. The paper presents the Chimera architecture, a hardware mapping strategy, and empirical evidence showing that neuro-symbolic attention primitives can achieve high-fidelity inference within the resource envelope of commodity programmable switches.

翻译：在可编程数据平面上直接部署表达能力强的学习模型有望实现线速、低延迟的流量分析，但严格的硬件约束以及对可预测、可审计行为的需求仍构成主要障碍。Chimera提出了一种原则性框架，将面向注意力的神经计算与符号约束映射到数据平面原语上，从而在匹配-操作流水线内实现可信推理。该框架结合了核化线性注意力近似、双层键选择层次结构以及级联融合机制，在保持神经表达能力的同时强制执行严格的符号保证。其设计包含硬件感知映射协议和双时间尺度更新方案，二者协同工作使得系统能在实际数据平面资源预算下实现稳定的线速操作。本文介绍了Chimera体系结构、硬件映射策略，并通过实证数据表明神经符号注意力原语能够在商用可编程交换机的资源约束范围内实现高保真推理。