SNN-MLIR: New Compiler Framework for Deploying Spiking Neural Networks Across Platforms
Researchers have developed snn-mlir, an MLIR dialect that compiles spiking neural networks (SNNs) from the Neuromorphic Intermediate Representation (NIR) format to bare-metal C code. The tool addresses fragmentation in SNN deployment by providing a unified compilation path across different frameworks and hardware targets. This work enables SNNs trained in various frameworks to be efficiently deployed on CPUs and embedded systems without external dependencies.
The paper introduces snn-mlir, an open-source compiler framework designed to standardize the deployment of spiking neural networks across different platforms. SNNs have been increasingly trained using diverse frameworks (SnnTorch, Lava, Norse, and others), each with proprietary model formats that complicate deployment. While the Neuromorphic Intermediate Representation (NIR) provides a common exchange format for trained SNN models, it does not address how to actually run these models on hardware. The snn-mlir dialect fills this gap by offering a small set of type-polymorphic operations that work with both floating-point and quantized data, enabling a single intermediate representation to serve both simulation and hardware-oriented deployment. The framework includes a Python front end that reads NIR files and automatically inserts rescaling operations for consistent quantization, then lowers the dialect to standard operations that produce self-contained C11 code compilable on any CPU or embedded target. Current evaluation covers feedforward, fully-connected networks with CPU backends, and the tool is released under Apache-2.0 license on GitHub.
What's missing
The paper does not discuss performance benchmarks comparing snn-mlir-generated code against native implementations or other SNN deployment frameworks, nor does it address support for recurrent or convolutional SNN architectures beyond the stated scope of feedforward networks.
What different sources said
- arXiv cs.LGCenter
Spiking Neural Network inference on FPGAs with hls4ml
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