arXiv:2606.09213v1 Announce Type: cross Abstract: 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 represe
The proliferation of various SNN frameworks necessitates a standardized compilation path for practical neuromorphic computing applications.
A common compilation standard for SNNs could accelerate the deployment and adoption of neuromorphic hardware and software, facilitating AI development.
This initiative attempts to bridge the gap between high-level SNN models and bare-metal execution, promising more efficient and portable SNN deployments.
- · Neuromorphic hardware developers
- · AI researchers using SNNs
- · Embedded AI systems
- · MLIR ecosystem
- · Vendors with proprietary SNN compilation flows
- · Frameworks unable to adopt common standards
More efficient and interoperable SNN deployment across diverse neuromorphic platforms.
Increased research and development into SNNs due to reduced deployment friction.
Potential for specialized SNN hardware to gain wider adoption, impacting overall AI compute paradigms.
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Read at arXiv cs.LG