Spiking neural networks (SNNs) are increasingly trained in a wide range of frameworks (SnnTorch, Lava, Norse, and others) each with its own model format. The Neuromorphic Intermediate Representation (NIR) addresses this fragmentation by providing a common, framework-independent format for exchanging trained SNN models. NIR solves the exchange problem, but it stops there. It provides a description of a network, not a path to running one. Each backend is still left to implement deployment on its own, with no shared, transformable compiler representation in between. This paper presents snn-mlir, an outof-tree MLIR dialect for SNNs together with a NIR-MLIR-C compilation bridge. The dialect provides a small set of typepolymorphic operations that work identically on floating-point (f32/f64) and quantized data, so a single intermediate representation serves both simulation and hardware-oriented deployment. A Python front end reads any NIR file and emits dialect IR, automatically inserting rescaling operations to keep quantization scales consistent across layers. A reference lowering pass converts the dialect to standard linalg and arith operations, from which the toolchain produces self-contained, dependency free C11 code that compiles and runs on any C-capable CPU or embedded target. We evaluate numerical fidelity against reference outputs, portability across CPU targets, and the cost of quantization. The current scope is feedforward, fully-connected networks with a CPU backend. snn-mlir is released as open source under the Apache-2.0 license with LLVM-exception and it is already available on Github.

翻译：脉冲神经网络（SNNs）正越来越多地在多种框架（如SnnTorch、Lava、Norse等）中训练，每种框架均使用自有模型格式。神经形态中间表示（NIR）通过提供通用的、框架无关的格式来交换已训练的SNN模型，解决了碎片化问题。NIR解决了交换问题，但也仅限于此：它仅提供网络描述，而非运行网络的方法。每个后端仍需自行实现部署，且缺乏共享的可转换编译器中间表示。本文提出snn-mlir，一种面向SNN的树外MLIR方言，并构建了NIR-MLIR-C编译桥梁。该方言提供一组小型类型多态操作，在浮点（f32/f64）和量化数据上行为一致，因此单一中间表示即可同时支持仿真和面向硬件的部署。Python前端可读取任意NIR文件并生成方言IR，自动插入重缩放操作以保持层间量化尺度一致。参考降级通道将该方言转换为标准linalg和arith操作，工具链由此生成自包含、无依赖的C11代码，可在任何支持C的CPU或嵌入式目标上编译运行。我们评估了相较参考输出的数值保真度、跨CPU目标的移植性以及量化成本。当前范围涵盖前馈全连接网络及CPU后端。snn-mlir已基于Apache-2.0许可证（含LLVM例外）开源发布，并已在Github上提供。