# Neuromorphic Chips Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/neuromorphic-chips
> Markdown URL: https://aitinkerers.org/technologies/neuromorphic-chips.md
> Technology record last updated: 2026-02-23T23:06:55Z
> Generated: 2026-09-22T12:38:54Z

Neuromorphic chips are brain-inspired processors: they use Spiking Neural Networks (SNNs) and event-driven computation to achieve ultra-low-power, high-speed AI at the edge.

This technology re-architects computing, moving past von Neumann bottlenecks by mimicking biological neurons and synapses. Chips like Intel’s Loihi 2 (1 million neurons, 128 cores) and IBM’s retired TrueNorth (1 million neurons, 256 million synapses) leverage asynchronous, event-driven processing: computation only occurs when data arrives (a ‘spike’). This model delivers orders-of-magnitude energy efficiency improvements over conventional GPUs or CPUs, operating at power levels as low as ~1W for Loihi 2. Neuromorphic chips are purpose-built for real-time, ultra-low-power applications, specifically robotics, autonomous systems, and always-on edge AI where latency and power consumption are critical factors.

- Official technology site: https://open-neuromorphic.org
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Neural Memory for AI](https://vienna.aitinkerers.org/talks/rsvp_7UkJKsoOXTM)

Miruvor AI is replacing Vector embeddings and Similarity search with a Brain Inspired Neuromorphic way to have AI Memory that is extremely low latency, energy efficient and allows to learn from Memories post training. By storing Memories into a Live Neural network and retrieval by Pattern recognition and reactivation, we can get rid of the latency and energy cost of similarity search. This is how the brain parallelizes associative memory allowing it to think and remember in milliseconds. We've innovated an architecture and algorithms to use local plasticity rules to learn from new memories and have temporal context, as opposed to using a database query for AI Memory. This is what will translate onto Edge AI and Neuromorphic Chips powering Physical AI as well. I'd be happy to present my Research Paper , architecture diagrams, technical pipelines on the same, show how these memories are ingested and associated. I'd be happy to talk all about the implementation, relavence to Drones, Robotics and how this can help AI Agents and more. We can show our MVP, how the API works, how this can be integrated within Agentic Workflows and used by Agentic AI Startups as well! Self learning AI can start here. We've benchmarked this on the locomo Benchmark and achieved 88% accuracy and 50ms average retrieval speed.

- Event context: AI Tinkerers Vienna: 2026 — 2026-02-19 — Vienna
- Public talk page: https://vienna.aitinkerers.org/talks/rsvp_7UkJKsoOXTM

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