Spikeforge

Spikeforge

Spikeforge is an open-source toolkit for building, training, inspecting, and deploying spiking neural networks, aimed at researchers and developers working with event-driven, always-on, or low-power sensing and robotics applications.

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Pricing not listed
#97 in Research (97 tools)
Just launched
Data reviewed Sep 14, 2026

Profile facts come from the vendor source. AiMatch labels unknown pricing or API details instead of estimating them.

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Quick Overview

Best for: Research

What it does

Research software for decision-makers comparing workflow fit and alternatives.

Best fit

Research

Pricing snapshot

Pricing available on request

Next step

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Spikeforge

Spikeforge is an open-source toolkit for spiking neural networks that aims to make event-driven, energy-efficient AI practical. It provides tools to encode data into spikes, train networks, inspect and compare topologies and deployment targets, and ship models. The toolkit targets researchers and developers working on always-on sensors, robots, cameras, audio tools, and other systems that benefit from event-driven, low-power processing.

Spikeforge is an open-source toolkit for building, training, inspecting, and deploying spiking neural networks.

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Key Features

Dashboard

Web-based dashboard to train, inspect, and deploy models in the browser (noted as 'Open dashboard' and 'Train, inspect, and deploy in the browser').

Encoding, Training, and Topologies

Components for encoding data into spikes, training spiking networks, and defining network topologies (listed as 'Encoding Training Topologies Datasets').

Model Hub

Model hub for architectures and (planned) pretrained weights, used to share and reuse models and components ('Model hub — architectures done' and 'Model hub — pretrained weights not started').

Interpreter & Introspection

Interpreter (spine) and introspection tools for inspecting network behavior and runtime ('Interpreter spine' and 'Introspection').

Deployment & Backends

Deployment tooling and multiple backends for target systems, including a lava_loihi2 backend and ongoing work for hardware backends ('lava_loihi2 backend done', 'speck / xylo / spinnaker2 in progress').

NIR Export & Validation

Support for NIR export and validation to aid deployment and compatibility ('NIR export & validation done').

Pipeline Builder & spikeforge-serve

Pipeline builder and a serve component for model serving and pipelines ('Pipeline builder done', 'spikeforge-serve done').

Package Distribution

Package distribution via PyPI is indicated as completed ('PyPI release done').

Pricing

Current pricing details are not available from the vendor source.

Use Cases

Always-on sensing

Build and deploy spiking models for always-on sensors that react to events while using less energy (supported by the page's description of energy-efficient event-driven operation).

Robotics and real-time control

Use spiking networks for robots and systems that need to respond quickly to changes ('robots' are explicitly mentioned as examples).

Edge audio and vision

Deploy spiking models for cameras and audio tools that benefit from event-driven processing ('cameras, and audio tools' are explicitly mentioned).

Research and development

Researchers and developers can train, inspect, compare deployment targets, and ship models using the unified toolkit.

Integrations

lava_loihi2 backend

Backend support for running on Loihi 2 via lava_loihi2 (listed as done).

speck / xylo / spinnaker2

Hardware/backends listed as in progress for additional deployment targets.

PyPI package

Package distribution via PyPI to allow integration into Python projects ('PyPI release done').

GitHub

Source, issues, releases and contributions are managed on GitHub (repository URL provided).

Benefits

Event-driven processing that emits signals only when meaningful changes occur, reducing unnecessary computation.
Lower energy usage for always-on and edge applications compared with continuously-sampling approaches.
Integrated toolchain for encoding, training, inspection, backend comparison, and deployment, reducing the need to assemble multiple tools.

Limitations

Model hub — pretrained weights: not started (pretrained weights are not yet available).
On-chip / local learning rules: work in progress (listed as 'in progress').
Several hardware backends (speck / xylo / spinnaker2) are in progress and not yet complete.

Frequently Asked Questions

No verified FAQs are available.

Getting Started

  1. 1 Step 1: Clone the repository: git clone https://github.com/capsize-games/spikeforge.git
  2. 2 Step 2: Install and set up locally: cd spikeforge && ./install.sh
  3. 3 Step 3: Open the dashboard to train, inspect, and deploy models or read the documentation for guides and examples.

Support

docs

Documentation, guides, reference, examples and plans are available via the 'Read the docs' link on the site.

GitHub

Source, issues, releases, and contributions are available on the project's GitHub repository.

dashboard

A web dashboard is available for training, inspection, deployment and viewing project status.

API

Available: No

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