San Diego, CA – 25 March 2026 – Innatera, a leader in neuromorphic computing for the sensor edge, today announced Synfire, an open, community-driven platform designed to unify and accelerate the neuromorphic ecosystem. The platform is open for registration and will be fully available in late April.

Introduced at Edge AI San Diego 2026, Synfire addresses one of the most critical barriers to scaling neuromorphic AI: fragmentation across tools, models, and deployment pipelines.

While neuromorphic hardware capabilities have rapidly matured, the path from research to real-world deployment still faces challenges due to a lack of interoperability, reproducibility, and standardized model exchange. Synfire directly tackles this challenge by providing a centralized, open repository for neuromorphic models and full processing pipelines. This enables developers to publish, discover, and deploy spiking neural network (SNN) solutions with significantly reduced friction.

“Progress for neuromorphic networks follows a clear path. First, we make them usable by building the infrastructure, tools, and shared foundations that allow people to work with them. Then, we make them useful, enabling them to solve real problems, adapt to real environments, and deliver real impact,” says Steve Furber, Professor Emeritus of Computer Engineering at the University of Manchester. “Creating a community-driven platform like Synfire, accelerates this transition by offering developers the opportunity to lead innovation in this space.”

Unlike general-purpose AI platforms, Synfire is purpose-built for neuromorphic computing. It supports the unique requirements of temporal, event-driven models and enables hardware-aware discovery, reproducibility metadata, and full pipeline packaging—from preprocessing and encoding to inference and actuation.

"Synfire is how neuromorphic computing gets out of the lab. Current AI hardware was not built for real-world intelligence. Neuromorphic systems are, but only if models, benchmarks, and hardware can speak the same language," notes Dr. Jens Egholm, Lead Author for Neuromorphic Intermediate Representation and Neuromorphic Computing Researcher. "The Neuromorphic Intermediate Representation (NIR) provided that shared language. Synfire builds the commons on top of it. Upload once, run anywhere, reproduce everything, and build on each other’s work to go further than before."

A Platform Designed for Real Deployment

Synfire introduces a new foundation for the neuromorphic community through:

• Open model registry for publishing and discovering SNN-based solutions
• Hardware-aware metadata to match models with validated execution targets
• Web platform, CLI, and SDK integration for seamless developer workflows
• Extensible architecture aligned with evolving standards such as NIR

A Community-Led Effort to Drive Convergence

Synfire is designed as a vendor-neutral open infrastructure, co-steered with broader industry and research communities.

“We have made incredible progress in neuromorphic hardware and model design, but the surrounding ecosystem is still fragmented. There is no consistent way to capture how a model was built, how it should run, or where it has been validated,” adds Petruț Antoniu Bogdan, Neuromorphic Architect at Innatera. “That makes reuse difficult and slows down real deployment. Synfire introduces structure to fill these gaps by standardizing how models are shared, while remaining flexible enough to evolve with the field. While perfecting the tools is a key milestone, our true goal is to build a coherent ecosystem that can efficiently build on top of published work and deploy to a variety of neuromorphic devices with minimal manual work, all the while maintaining the original model performance.”

Moving from Fragmentation to Scale

Synfire is a fundamental shift in how neuromorphic systems are built, shared, and deployed, unlocking faster innovation across industries, including smart sensing, industrial automation, healthcare, and consumer devices.

Join Synfire via early registration and help build the next generation of neuromorphic systems.