Documentation and resources from all Working Groups
Mar 5, 2026 Attended: @petrut-bogdan steve.furber@manchester.ac.uk Pete Bernard EAIF Team Calendar Jonathan Tapson @massimiliano-versace Attachments EDGE AI Neuromorphic WG EDGE AI Neuromorphic WG kick off meeting - 2025/09/29 08:56 CST - Notes by Gemini Summary Spin Cloud and neuromorphic viability were discussed, with a significant update on the primitive software
The goal of the EDGE AI FOUNDATION is to build awareness and share best practices for implementing Edge AI solutions. The first deliverable for the community’s recently-formed Commercialization Working Group is a white paper that breaks down a comprehensive taxonomy for the Edge AI continuum, key considerations for real-world implementation,
Invited Pete Bernard Eric Smiley Jason Shepherd Rosina Haberl Michael Kuptz Woolley, Rob Summary Contact List and Document UpdatesThe team needs updated contact information for participating companies and confirmed that documentation updates are required, including addressing terminology shifts like avoiding the term "EAif".Defining Edge Taxonomy and ContentThe discussion centered on
Invited Pete Bernard Jason Shepherd Rosina Haberl Michael Kuptz Eric Smiley Woolley, Rob Summary White Paper Consensus and ContentThe group interpreted the lack of feedback on the white paper approach as consensus and agreed to proceed with the taxonomy-based format. Initial content additions focused on filling out the dimensions table
Feb 27, 2026 Invited Pete Bernard Jason Shepherd Rosina Haberl Michael Kuptz Eric Smiley Woolley, Rob Attachments EDGE AI Commercialization WG Summary Document release strategy finalized with a preview blog post before Embedded World and content submission deadlines extended for final tuningDocument Release Strategy AdoptedThe team decided to release a
MoM feb 27 2026 GenEdgeAI WGDownload
LFM2-24B-A2B, a 24B Mixture-of-Experts model with 2.3B parameters active per token, built on Liquid AI hybrid, hardware-aware LFM2 architecture.By activating only the most relevant parameters at runtime, LFM2-24B-A2B delivers large-model capability with fast, memory-efficient behavior in a 32GB, 2B-active footprint. https://huggingface.co/LiquidAI/LFM2-24B-A2B-GGUF Check ollama https://ollama.com/search?o=newest
Detecting and preventing distillation attacks \ Anthropic Anthropic (2/23) identified industrial-scale distillation (not subject to export control) campaigns by three AI foreigner laboratories.The only way to train edge LM with agentic reasoning ?
