# Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing

arXiv: https://arxiv.org/abs/2311.14641 · HTML: https://mlsanity.com/paper/2311.14641/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2023.

## Built on by (most cited first, 22 of 22)

- [SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning](https://mlsanity.com/paper/2401.04491/) (2024)
- [Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation](https://mlsanity.com/paper/2504.06748/) (2025)
- [Wandering around: A bioinspired approach to visual attention through object motion sensitivity](https://mlsanity.com/paper/2502.06747/) (2025)
- [Covariant spatio-temporal receptive fields for spiking neural networks](https://mlsanity.com/paper/2405.00318/) (2024)
- [Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware](https://mlsanity.com/paper/2405.01305/) (2024)
- [SNNAX -- Spiking Neural Networks in JAX](https://mlsanity.com/paper/2409.02842/) (2024)
- [Spyx: A Library for Just-In-Time Compiled Optimization of Spiking Neural Networks](https://mlsanity.com/paper/2402.18994/) (2024)
- [SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networks](https://mlsanity.com/paper/2404.19668/) (2024)
- [An Integrated Toolbox for Creating Neuromorphic Edge Applications](https://mlsanity.com/paper/2404.08726/) (2024)
- [A Truly Sparse and General Implementation of Gradient-Based Synaptic Plasticity](https://mlsanity.com/paper/2501.11407/) (2025)
- [Neuromorphic Intelligence](https://mlsanity.com/paper/2509.11940/) (2025)
- [Neuromorphic Programming: Emerging Directions for Brain-Inspired Hardware](https://mlsanity.com/paper/2410.22352/) (2024)
- [Application-oriented automatic hyperparameter optimization for spiking neural network prototyping](https://mlsanity.com/paper/2502.12172/) (2025)
- [FeNN-DMA: A RISC-V SoC for SNN acceleration](https://mlsanity.com/paper/2511.00732/) (2025)
- [Heterogeneous computing platform for real-time robotics](https://mlsanity.com/paper/2601.09755/) (2026)
- [Scale-covariant spiking wavelets](https://mlsanity.com/paper/2602.02020/) (2026)
- [AceleradorSNN: A Neuromorphic Cognitive System Integrating Spiking Neural Networks and DynamicImage Signal Processing on FPGA](https://mlsanity.com/paper/2603.28429/) (2026)
- [Heterogeneous SoC Integrating an Open-Source Recurrent SNN Accelerator for Neuromorphic Edge Computing on FPGA](https://mlsanity.com/paper/2605.12217/) (2026)
- [SNN-MLIR: An MLIR Dialect for Compiling Neuromorphic SNNs from NIR to Bare-Metal C](https://mlsanity.com/paper/2606.09213/) (2026)
- [Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation](https://mlsanity.com/paper/2606.28600/) (2026)
- [The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing](https://mlsanity.com/paper/2607.24396/) (2026)
- [Syn2Logic: End-to-End Neuromorphic Design Automation](https://mlsanity.com/paper/2608.25536/) (2026)

## Abstract (arXiv)

Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available at neuroir.org