Brainchip CES 2025 page features LLMs + RAG, Anomoly Detectiob. and Building ML Models with Edge Impulse.
Brainchip CES 2025
https://brainchip.com/ces-2025/
The Edge Impulse partnership is essential for rapid deployment of Akida. NNs require appropriate task-specific models to perform specific tasks. Before Edge Impulse, the models were hand made, a time-consuming and costly task. With EI, models can be assembled automatically using customer data and general models in the same field as the customer's specific tasks.
Then, of course, the models need to be converted to Akida-ese (the spike coding format required for Akida models).
LLMs and RAG, as far as I understand it, means dividing a large universal LLM into subject-specific blocks, and selecting the block or blocks required for the task in hand and using the selected block(s) in configuring Akida. Again, for this to work with Akida the blocks need to be in Akida-usable format, so I would suspect the presence of Edge Impulse at some stage of the process.
https://edgeimpulse.com/all-events/edge-impulse-at-ces
The Edge Impulse team is excited to head back to Vegas in January for another exciting CES!
Our team will be exhibiting with our partners BrainChip, Microchip and CEVA, in addition to walking the floors and meeting with prospects and customers.
Brainchip CES 2025
https://brainchip.com/ces-2025/
LLMs + RAG Demo
See how we’re advancing large language models (LLMs) with Retrieval-Augmented Generation (RAG) for smarter, real-time AI applications.Anomaly Detection Demo
Explore our latest anomaly detection solution running on Raspberry Pi 5. This versatile demo targets multiple verticals, including Industrial IoT, manufacturing, healthcare (wearable devices), cybersecurity, fraud detection, and more.Building ML Models with Edge Impulse
Explore hands-on demos with Edge Impulse, demonstrating how easy it is to build and deploy custom machine learning models directly on the Akida platform.The Edge Impulse partnership is essential for rapid deployment of Akida. NNs require appropriate task-specific models to perform specific tasks. Before Edge Impulse, the models were hand made, a time-consuming and costly task. With EI, models can be assembled automatically using customer data and general models in the same field as the customer's specific tasks.
Then, of course, the models need to be converted to Akida-ese (the spike coding format required for Akida models).
LLMs and RAG, as far as I understand it, means dividing a large universal LLM into subject-specific blocks, and selecting the block or blocks required for the task in hand and using the selected block(s) in configuring Akida. Again, for this to work with Akida the blocks need to be in Akida-usable format, so I would suspect the presence of Edge Impulse at some stage of the process.
https://edgeimpulse.com/all-events/edge-impulse-at-ces
The Edge Impulse team is excited to head back to Vegas in January for another exciting CES!
Our team will be exhibiting with our partners BrainChip, Microchip and CEVA, in addition to walking the floors and meeting with prospects and customers.
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