BRN Discussion Ongoing

[7für7]

Ericsson, Intel, 6 G.

Technical Synergy and Neuromorphic AI in 6G
The evolution toward 6G requires "Networks for AI," where the infrastructure itself is programmable and capable of real-time sensing.[1] Neuromorphic computing, as detailed in academic literature, offers a path toward the "extreme low power" requirements of 6G sensing nodes.[10] BrainChip’s technology enables on-chip learning independent of the cloud, which aligns with the Ericsson-Intel goal of reducing latency and improving data security at the network edge

I can’t find any mention of BrainChip in either the post itself or in the link it points to.

Bringing BrainChip into this without any source is, at best, speculation based on wishful thinking … and at worst, an attempt to mislead people.

Unless someone can provide a direct reference …official announcement, filing, press release, or an explicit mention by the author…, this should be treated as pure guesswork.

My opinion only

 

[smoothsailing18]

Asking Ai it's thoughts on the linkden post and that's a snippit of its response

 

[Diogenese]

My wife kicked me in the vernaculars once - I sympathise

After some alphabet soup, I past participles.

 

[Rach2512]

I couldn’t find that snippet you posted in the LinkedIn article. Can you chuck a link up.

Ericsson and Intel collaborate to accelerate the path to commercial AI-native 6G | Intel

Announced at #MWC26, Ericsson and Intel are extending a decades-long collaboration to accelerate the path to commercial AI-native 6G. Designed to help the industry move from 6G research to commercial reality, the effort spans AI-driven RAN, packet core and edge use cases across compute...

www.linkedin.com

 

[7für7]

Ericsson and Intel collaborate to accelerate the path to commercial AI-native 6G | Intel

Announced at #MWC26, Ericsson and Intel are extending a decades-long collaboration to accelerate the path to commercial AI-native 6G. Designed to help the industry move from 6G research to commercial reality, the effort spans AI-driven RAN, packet core and edge use cases across compute...

www.linkedin.com

He said…

“I couldn’t find that snippet you posted in the LinkedIn article. Can you chuck a link up.”

Not the link of intel… any link referring his statement about brainchip… but as we know …there is no link because it was a ChatGPT answer

 

[smoothsailing18]

FF

Possible use cases as posited by Google Ai:

“Based on GBrain's core business in neurotechnology and BrainChip's current technical roadmap, their collaboration likely focuses on the following uses of Akidatechnology:

1. Ultra-Low-Power Seizure & Event Detection
GBrain develops graphene-based neural implants (Phin Array) for monitoring brain activity in patients with epilepsy.
BrainChip
The Akida Use Case: Akida's event-based architecture is designed to remain in an "always-on" state while consuming micro-watts of power.
Benefit: GBrain can use Akida to process neural spikes directly on the implant, identifying the onset of a seizure in real-time without the latency or power drain of transmitting raw data to an external device.
BrainChip +3

2. On-Device Personalization (Learning)
A key differentiator of the Akida processor is its ability to perform on-chip learning.
BrainChip
The Akida Use Case: Every patient's neural signatures are unique.
Benefit: GBrain can leverage Akida to "learn" a specific patient's normal brain patterns versus pathological activity on the fly. This allows the medical implant to adapt its detection algorithms to the individual user without requiring cloud-based retraining.
BrainChip

3. Smart Brain Stimulation (Phin Stim)
GBrain's Phin Stim is a wireless stimulator used to treat motor diseases.
AFR
The Akida Use Case: Akida 2.0 and the newer TENNs (Temporal Event-based Neural Networks) are optimized for processing time-series data like EEG or ECoG.
Benefit: By integrating Akida, the Phin Stim can become a "closed-loop" system. Akida identifies the precise moment stimulation is needed (inference) and triggers the electrode immediately, maximizing therapeutic effect while saving battery life.
BrainChip +3

4. AKD1500 & AKD2500 Integration
BrainChip recently initiated the AKD2500 custom silicon projectin February 2026.
**promotion blocked**.com.au
The Akida Use Case: Enablement partners like GBrain typically get early access to these next-gen platforms via the AI Enablement Package.
Benefit: This provides GBrain with the most advanced 12nm neuromorphic IP to minimize the physical footprint of their implants, making them less invasive for patients

 

[7für7]

FF

Possible use cases as posited by Google Ai:

“Based on GBrain's core business in neurotechnology and BrainChip's current technical roadmap, their collaboration likely focuses on the following uses of Akidatechnology:

1. Ultra-Low-Power Seizure & Event Detection
GBrain develops graphene-based neural implants (Phin Array) for monitoring brain activity in patients with epilepsy.
BrainChip
The Akida Use Case: Akida's event-based architecture is designed to remain in an "always-on" state while consuming micro-watts of power.
Benefit: GBrain can use Akida to process neural spikes directly on the implant, identifying the onset of a seizure in real-time without the latency or power drain of transmitting raw data to an external device.
BrainChip +3

2. On-Device Personalization (Learning)
A key differentiator of the Akida processor is its ability to perform on-chip learning.
BrainChip
The Akida Use Case: Every patient's neural signatures are unique.
Benefit: GBrain can leverage Akida to "learn" a specific patient's normal brain patterns versus pathological activity on the fly. This allows the medical implant to adapt its detection algorithms to the individual user without requiring cloud-based retraining.
BrainChip

3. Smart Brain Stimulation (Phin Stim)
GBrain's Phin Stim is a wireless stimulator used to treat motor diseases.
AFR
The Akida Use Case: Akida 2.0 and the newer TENNs (Temporal Event-based Neural Networks) are optimized for processing time-series data like EEG or ECoG.
Benefit: By integrating Akida, the Phin Stim can become a "closed-loop" system. Akida identifies the precise moment stimulation is needed (inference) and triggers the electrode immediately, maximizing therapeutic effect while saving battery life.
BrainChip +3

4. AKD1500 & AKD2500 Integration
BrainChip recently initiated the AKD2500 custom silicon projectin February 2026.
**promotion blocked**.com.au
The Akida Use Case: Enablement partners like GBrain typically get early access to these next-gen platforms via the AI Enablement Package.
Benefit: This provides GBrain with the most advanced 12nm neuromorphic IP to minimize the physical footprint of their implants, making them less invasive for patients

Interesting how he used to smash down everyone who used AI for analysis regarding akida, now he post stuff form AI-created opinions by himself…

My opinion only

 

[Fullmoonfever]

Ericsson, Intel, 6 G.

Technical Synergy and Neuromorphic AI in 6G
The evolution toward 6G requires "Networks for AI," where the infrastructure itself is programmable and capable of real-time sensing.[1] Neuromorphic computing, as detailed in academic literature, offers a path toward the "extreme low power" requirements of 6G sensing nodes.[10] BrainChip’s technology enables on-chip learning independent of the cloud, which aligns with the Ericsson-Intel goal of reducing latency and improving data security at the network edge

We also know that Ericsson were playing with Akida late 2023 for 6G

BRN Discussion Ongoing

Hi DB, There are 2 distinct issues: 1. Invalidation of Oculi's patent claims by prior publication: Whether the Oculi patent claims something which is inventive over and above what is disclosed in the Dio I just want to wish you a Xmas greeting and publicly thank you for your knowledge...

thestockexchange.com.au

 

[Rach2512]

#eventcamera | Davide Scaramuzza

We are excited to share our work “Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones” published in IEEE Transactions on Robotics, which tackles sharp radiance field reconstruction under agile drone motion, where RGB frames are heavily motion-blurred and pose priors become...

www.linkedin.com