# An Integrated Toolbox for Creating Neuromorphic Edge Applications

arXiv: https://arxiv.org/abs/2404.08726 · HTML: https://mlsanity.com/paper/2404.08726/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 80 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing](https://mlsanity.com/paper/2311.14641/) (2023)
- [Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack](https://mlsanity.com/paper/2404.03325/) (2024)
- [PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network](https://mlsanity.com/paper/2003.09696/) (2020)
- [NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks](https://mlsanity.com/paper/2105.01795/) (2021)
- [Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware](https://mlsanity.com/paper/2103.05707/) (2021)
- [Enhancing software module reusability using port plug-ins: an experiment with the iCub robot](https://mlsanity.com/paper/1411.1102/) (2014)
- [Communication channel prioritization in a publish-subscribe architecture](https://mlsanity.com/paper/1504.02128/) (2015)

## Built on by (most cited first, 1 of 1)

- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)

## Abstract (arXiv)

Spiking Neural Networks (SNNs) and neuromorphic models are more efficient and have more biological realism than the activation functions typically used in deep neural networks, transformer models and generative AI. SNNs have local learning rules, are able to learn on small data sets, and can adapt through neuromodulation. Although research has shown their advantages, there are still few compelling practical applications, especially at the edge where sensors and actuators need to be processed in a timely fashion. One reason for this might be that SNNs are much more challenging to understand, build, and operate due to their intrinsic properties. For instance, the mathematical foundation involves differential equations rather than basic activation functions. To address these challenges, we have developed CARLsim++. It is an integrated toolbox that enables fast and easy creation of neuromorphic applications. It encapsulates the mathematical intrinsics and low-level C++ programming by providing a graphical user interface for users who do not have a background in software engineering but still want to create neuromorphic models. Developers can easily configure inputs and outputs to devices and robots. These can be accurately simulated before deploying on physical devices. CARLsim++ can lead to rapid development of neuromorphic applications for simulation or edge processing.