feat: Add NESTML, NIR, and KNP content

Adds markdown files and logos for NESTML, NIR, and KNP.

Author: neural-loop

.github/workflows/main.yml

@@ -2,7 +2,7 @@ name: Deploy Hugo site to Pages
 
 on:
   push:
-    branches: ["main", "student-talk"]
+    branches: ["main", "software-entries"]
 
 permissions:
   contents: read

content/neuromorphic-computing/software/data-tools/neuromorphic-intermediate-representation/index.md

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+---
+title: "Neuromorphic Intermediate Representation (NIR)"
+type: "neuromorphic-software"
+description: "A graph-based intermediate representation for computational graphs of spiking neural networks, enabling interoperability across different simulators and hardware."
+logo: "nir-logo.png"
+website: "https://neuromorphs.github.io/nir/"
+dependencies: "Numpy"
+field_of_application: "Interoperability / SNN Model Exchange"
+source_code: "https://github.com/neuromorphs/NIR"
+pypi_id: "nir"
+license: "MIT"
+supports_hardware: True
+supports_NIR: True
+language: "Python"
+maintainer:
+  - "Jens E. Pedersen"
+  - "Felix Bauer"
+  - "Jason Eshraghian"
+  - "Bernhard Vogginger"
+draft: false
+---
+
+## Overview
+
+The Neuromorphic Intermediate Representation (NIR) is a graph-based format designed to represent Spiking Neural Network (SNN) models in a standardized way. Its primary goal is to enable interoperability between a wide variety of neuromorphic simulators and hardware platforms.
+
+By defining a common set of computational primitives (like Leaky-Integrate-and-Fire neurons and synapses), NIR allows researchers and developers to define a model once and then translate it to run on different backends without having to rewrite the model from scratch for each platform. This decouples the model definition from the hardware- or software-specific implementation details.
+
+NIR is designed to be extensible and currently supports a range of popular SNN frameworks and hardware, including:
+-   **Simulators:** Spyx, snnTorch, Norse, Lava
+-   **Hardware:** Intel Loihi 2, SpiNNaker 2, Speck
+
+The project aims to simplify the workflow for neuromorphic development, making it easier to benchmark, deploy, and share SNN models across the community.

content/neuromorphic-computing/software/data-tools/neuromorphic-intermediate-representation/nir-logo.png

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+---
+title: "Kaspersky Neuromorphic Platform"
+type: "neuromorphic-software"
+description: "A platform for creating and training Spiking Neural Networks (SNNs), supporting various data types and neuromorphic processors."
+logo: "knp-logo.png"
+website: "https://github.com/KasperskyLab/knp"
+dependencies: "Python, C++, Boost, CMake, NumPy, TensorFlow, Keras"
+field_of_application: "Machine Learning"
+source_code: "https://github.com/KasperskyLab/knp"
+license: "Apache-2.0"
+supports_hardware: True
+version_badge_url_override: "https://img.shields.io/github/v/tag/KasperskyLab/knp"
+supports_NIR: False
+language: "Python, C++"
+maintainer: "Kaspersky"
+draft: false
+---
+
+## Overview
+The Kaspersky Neuromorphic Platform (KNP) is a software platform for developing, training, and executing Spiking Neural Networks (SNNs). It is designed to work with various types of input data, including telemetry, events, images, 3D data, audio, and tactile data. KNP allows users to create and train SNNs, convert Artificial Neural Networks (ANNs) into SNNs, and optimize network structures.
+The platform supports application development in both C++ and Python and can be deployed on standard CPUs as well as the AltAI-1 neuromorphic processor. This makes it suitable for a range of applications, from research into new neural network topologies and learning rules to implementing solutions for robotics, IoT, and other low-power devices.
+Key features include:
+Creation and training of SNNs.
+Conversion of ANNs to SNNs.
+Development of new neuron models and synaptic plasticity rules.
+Support for both CPU and neuromorphic hardware (AltAI-1).
+Aimed at applications in robotics, IoT, unmanned systems, and wearable devices.

content/neuromorphic-computing/software/snn-frameworks/knp/index.md

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+---
+title: "NESTML"
+type: neuromorphic-software
+description:  A domain-specific language and code generation toolchain for neuron and synapse models in spiking neural network simulation
+logo: nestml.png
+website: https://nestml.readthedocs.org/
+dependencies: Python, Jinja2, Lark, SymPy
+field_of_application: Neuroscience
+source_code: https://github.com/nest/nestml
+pypi_id: nestml
+license: GPL-2.0
+supports_hardware: True
+supports_NIR: False
+language: Python
+maintainer: Charl Linssen
+draft: false
+---
+
+
+## Overview
+NESTML is a domain-specific language for neuron and synapse models. These dynamical models can be used in simulations of brain activity on several platforms, in particular the [NEST Simulator](https://nest-simulator.readthedocs.org/). NESTML combines an easy to understand, yet powerful syntax; a flexible processing toolchain, written in Python; and good simulation performance by means of code generation (C++ for NEST Simulator).