5 to 10 years ... 2030..2035 maybeA fellow forum user who in recent months repeatedly referred to his brief LinkedIn exchange with Mercedes-Benz Chief Software Officer Magnus Östberg (and thereby willingly revealed his identity to all of us here on TSE, which in turn means I’m not guilty of spilling a secret with this post that should have been kept private), asked Mercedes-Benz a question in the comment section underneath the company’s latest LinkedIn post on neuromorphic computing. This time, however, he decided not to share the carmaker’s reply with all of us here on TSE. You gotta wonder why.
Could it possibly have to do with the fact that MB’s reply refutes the hypothesis he had been advancing for months, namely that Mercedes-Benz, who have been heavily promoting their future SDV (software defined vehicle) approach that gives them the option of OTA (over-the-air) updates, would “more than likely” have used Akida 2.0/TENNs simulation software in the upcoming MB.OS release as an interim solution during ongoing development until the not-yet existing Akida 2.0 silicon became available at a later stage? The underlying reason being competitive pressure to be first-to-market…
The way I see it, the January 29 reply by MB clearly puts this speculation to bed:
The intelligent vehicle functionalities of the future call for pioneering new algorithms and hardware. | Mercedes-Benz AG
The intelligent vehicle functionalities of the future call for pioneering new algorithms and hardware. That’s why Mercedes-Benz is researching artificial neural networks that could radically increase the speed and energy efficiency of complex AI computations. This could revolutionise...www.linkedin.com
View attachment 77012
Does that sound as if an MB.OS “Akida inside” reveal at the upcoming world premiere of the CLA were on the cards?
Setting aside the questions
a) about any requirements for testing and certification of car parts making up the infotainment system (being used to German/EU bureaucracy, I find it hard to believe there wouldn’t be any at all - maybe someone who is knowledgeable about automotive regulations within Germany and the EU could comment on this) and
b) whether any new MB model containing our tech could roll off the production line despite no prior IP license deal having been signed (or at least an Akida 1.0 chips sales deal; there has never been a joint development announcement either which could possibly somehow circumvent the necessity of an upfront payment showing up in our financials)….
… various MB statements in recent months (cf. Dominik Blum’s presentation at HKA Karlsruhe I shared in October: https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-439352 - the university of applied sciences they have since confirmed to cooperate with regarding research on neuromorphic cameras, journalists quoting MB engineers after having visited their Future Technologies Lab as well as relevant posts and comments on LinkedIn) have diminished the likelihood of neuromorphic tech making its debut in any soon to be released Mercedes-Benz models.
If NC were to enhance voice control and infotainment functions in their production vehicles much sooner than safety-critical ones (ADAS), MB would surely have clarified this in their reply to the above question posed to them on LinkedIn, which specifically referred to the soon-to-be released CLA, which happens to be the first model to come with the next-generation MB.OS that also boasts the new AI-powered MBUX Virtual Assistant (developed in collaboration with Google).
Instead, they literally wrote:
“(…) To fully leverage the potential of neuromorphic processes, specialised hardware architectures that efficiently mimic biologically inspired systems are required (…) we’re currently looking into Neuromorphic Computing as part of a research project. Depending on the further development progress, integration could become possible within a timeframe of 5 to 10 years.”
They are evidently exploring full scale integration to maximise the benefits of energy efficiency, latency and privacy. The voice control implementation of Akida in the Vision EQXX was their initial proof-of-concept to demonstrate feasibility of NC in general (cf. the podcast with Steven Peters, MB’s former Head of AI Research from 2016-2022: https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-407798). Whether they’ll eventually partner with us or a competitor (provided they are happy with their research project’s results) remains to be seen.
So I certainly do not expect the soon-to-be revealed CLA 2025 with the all-new MB.OS to have “Akida inside”, although I’d be more than happy to be proven wrong, as we’d all love to see the BRN share price soar on these news…
Time - and the financials - will ultimately tell.
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
Fullmoonfever
Top 20
Couldn't find this conference paper from 2024 posted but maybe my search didn't capture it.
Anyway, was a positive presentation at a IEEE conference and below are a couple of snips and as they advise, "This is an accepted manuscript version of a paper before final publisher editing and formatting. Archived with thanks to IEEE."
HERE
As the authors acknowledge:
"This work has been partially supported by King Abdullah University of Science and Technology CRG program under grant number: URF/1/4704-01-01.
We would like also to thank Edge Impulse and Brainchip companies for providing us with the software tools and hardware platform used during this work.*
One of the authors, M E Fouda, caught my eye given his/her relationship with "3", maybe employer...hmmmm.
D. A. Silva1
, A. Shymyrbay1
, K. Smagulova1
, A. Elsheikh2
, M. E. Fouda3,†
and A. M. Eltawil1
1 Department of ECE, CEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
2 Department of Mathematics and Engineering Physics, Faculty of Engineering, Cairo University, Giza 12613, Egypt
3 Rain Neuromorphics, Inc., San Francisco, CA, 94110, USA
†Email:foudam@uci.edu
End-to-End Edge Neuromorphic Object Detection System
Abstract—Neuromorphic accelerators are emerging as a potential solution to the growing power demands of Artificial
Intelligence (AI) applications. Spiking Neural Networks (SNNs), which are bio-inspired architectures, are being considered as a way to address this issue. Neuromorphic cameras, which operate on a similar principle, have also been developed, offering low power consumption, microsecond latency, and robustness in various lighting conditions.
This work presents a full neuromorphic
system for Computer Vision, from the camera to the processing hardware, with a focus on object detection. The system was evaluated on a compiled real-world dataset and a new synthetic dataset generated from existing videos, and it demonstrated good performance in both cases. The system was able to make accurate predictions while consuming 66mW, with a sparsity of 83%, and a time response of 138ms.
VI. CONCLUSION AND FUTURE WORK
This work showed a low-power and real-time latency full spiking neuromorphic system for object detection based on IniVation’s DVXplorer Lite event-based camera and Brainchip’s Akida AKD1000 spiking platform. The system was evaluated on three different datasets, comprising real-world and synthetic samples. The final mapped model achieved mAPs of 28.58 for the GEN1 dataset, equivalent to 54% of a more complex state of-the-art model and 89% of the performance detection from the best-reported result for the single-class dataset PEDRo, having 17x less parameters. A power consumption of 66mW and a latency of 138.88ms were reported, being suitable for real-time edge applications.
For future works, different models are expected to be adapted to the Akida platform, from which more recent
releases of the YOLO family can be implemented. Moreover, it is expected to evaluate those models in real-world scenarios instead of recordings, as well as the acquisition of more data to evaluate this setup under different challenging situations.
Anyway, was a positive presentation at a IEEE conference and below are a couple of snips and as they advise, "This is an accepted manuscript version of a paper before final publisher editing and formatting. Archived with thanks to IEEE."
HERE
As the authors acknowledge:
"This work has been partially supported by King Abdullah University of Science and Technology CRG program under grant number: URF/1/4704-01-01.
We would like also to thank Edge Impulse and Brainchip companies for providing us with the software tools and hardware platform used during this work.*
One of the authors, M E Fouda, caught my eye given his/her relationship with "3", maybe employer...hmmmm.
D. A. Silva1
, A. Shymyrbay1
, K. Smagulova1
, A. Elsheikh2
, M. E. Fouda3,†
and A. M. Eltawil1
1 Department of ECE, CEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
2 Department of Mathematics and Engineering Physics, Faculty of Engineering, Cairo University, Giza 12613, Egypt
3 Rain Neuromorphics, Inc., San Francisco, CA, 94110, USA
†Email:foudam@uci.edu
End-to-End Edge Neuromorphic Object Detection System
Abstract—Neuromorphic accelerators are emerging as a potential solution to the growing power demands of Artificial
Intelligence (AI) applications. Spiking Neural Networks (SNNs), which are bio-inspired architectures, are being considered as a way to address this issue. Neuromorphic cameras, which operate on a similar principle, have also been developed, offering low power consumption, microsecond latency, and robustness in various lighting conditions.
This work presents a full neuromorphic
system for Computer Vision, from the camera to the processing hardware, with a focus on object detection. The system was evaluated on a compiled real-world dataset and a new synthetic dataset generated from existing videos, and it demonstrated good performance in both cases. The system was able to make accurate predictions while consuming 66mW, with a sparsity of 83%, and a time response of 138ms.
VI. CONCLUSION AND FUTURE WORK
This work showed a low-power and real-time latency full spiking neuromorphic system for object detection based on IniVation’s DVXplorer Lite event-based camera and Brainchip’s Akida AKD1000 spiking platform. The system was evaluated on three different datasets, comprising real-world and synthetic samples. The final mapped model achieved mAPs of 28.58 for the GEN1 dataset, equivalent to 54% of a more complex state of-the-art model and 89% of the performance detection from the best-reported result for the single-class dataset PEDRo, having 17x less parameters. A power consumption of 66mW and a latency of 138.88ms were reported, being suitable for real-time edge applications.
For future works, different models are expected to be adapted to the Akida platform, from which more recent
releases of the YOLO family can be implemented. Moreover, it is expected to evaluate those models in real-world scenarios instead of recordings, as well as the acquisition of more data to evaluate this setup under different challenging situations.
Baneino
Regular
In my opinion, we should not become too fixated on Mercedes-Benz, because our major growth will not depend on whether we are a partner or whether Mercedes-Benz or a customer, but the many individual customers will be the straw that breaks the camel's back. And our technology will be used by thousands of companies at some point and they will pay for it. Mercedes-Benz is a well-known company, but I wouldn't put too much weight on it.
Best regards from Germany
Best regards from Germany
GazDix
Regular
Hi HG.
I emailed Investor Relations and they haven't responded yet.
Every quarter we get one, sometimes later than usual.
Fullmoonfever
Top 20
Another positive paper from 2024 I hadn't seen before but maybe posted somewhere.
Interesting small satellite project group appearing to be working with NASA and also have another related paper which I'm pretty has been discussed before...Brainsat.
This paper:
HERE
Who is the Space Generation Advisory Council?
spacegeneration.org
Members Globally
165+
Countries Worldwide
30+
SGAC Events Annually
11
Active Project Groups
Spiking Neural Network Design for on-board detection of methane emissions through Neuromorphic Computing
Andrew Karima∗
, Amel AlKholeifya
, Jimin Choia
, Jatin Dhalla
, Tan Hudaa
, Arnav Ranjekara
,
Yassine Yousfia
, Daniel Wischerta
a Space Generation Advisory Council, Small Satellite Project Group
* Corresponding author ( andrew.karim@spacegeneration.org andrew.karim@spacegeneration.org andrew.karim@spacegeneration.org)
Abstract
Small satellite constellations have shown tremendous success for Earth observation missions and can mimic the
performance of large satellite platforms while being cheaper and faster to deploy.
Detecting fugitive methane emissions
from ageing oil and gas infrastructures is an important use-case, as it helps facility operators to locate and mitigate these
leaks, ultimately addressing the global climate crisis. For such applications, it is crucial for the data to be transmitted
to the end-user in a timely manner for appropriate actions to take place. Edge Artificial Intelligence (AI) can address
this issue by pre-processing the data on-board the spacecraft, dramatically reducing the amount of data to be sent to
the ground and thus improving response times and bandwidth. As small satellites are greatly constrained by a low size,
weight and power (SWaP), their ability to process AI algorithms with fewer computational resources is essential.
AI applications often depend on real time data processing and analysis, yet most modern computers are inefficient in
these tasks. Neuromorphic Computing’s (NC) parallel and distributed architecture helps perform complex calculations
faster while using less power, enabling more efficient in-orbit data processing and onboard adaptive learning. This paper evaluates the potential of a novel 6U CubeSat neuromorphic on-board computer, applied to a case study for point-source methane emissions monitoring. It involves the design of a Spiking Neural Network (SNN) tailored for small satellite platforms.
The necessary processing steps for detection of point-source methane emissions using hyperspectral imagery are presented, and the potential of edge computing is discussed. A full processing pipeline enabling real-time monitoring
is proposed, which involved an SNN for segmentation of methane plumes. The architectural specifications necessary for
neuromorphic computing in the resource-constrained satellite environment are outlined. NC principles are incorporated
into the SNN architecture design, leveraging event-driven computation to improve energy efficiency and computational throughput. The processor’s performance is compared with traditional computing approaches.
Finally, a plan to train the model using an annotated dataset from the AVIRIS-NG hyperspectral instrument is presented, although training and validation are left for future work.
By embracing neuromorphic computing, an innovative approach inspired by the brain, satellites can achieve unparalleled efficiency and accuracy. The SNN model presented contributes to the scarce body of research on edge AI for greenhouse gas monitoring, providing a step forward towards real-time Earth observation.
Excerpt:
The designed OBP includes two PC104 modules, connected through a mezzanine connector. It integrates both CPU and FPGA capabilities and can cater for on board computer functions, payload data processing and down link management. The OBP is equipped with the Akida
1000 neuromorphic processor, selected for its design maturity, real-time processing capabilities and flight heritage.
The chip is tailored for event-based processing, featuring 80 Neuromorphic Processing Units (NPUs) with 100 KB
of SRAM each, supporting up to 1.2 million virtual neurons and 10 billion virtual synapses with up to 8-bit pre-
cision, ideal for inference-heavy tasks. An FPGA provides glue logic to implement necessary data protocols and serve as a soft CPU for OBC functions.
Additionally, a 12GB flight-proven Micron Solid State Device (SSD) is included to provide non-volatile on-board memory. This sub-system is constrained to a 0.5 U volume and is estimated to consume a low power of less than 4 W.
The processor’s software architecture was designed to minimize resource consumption. It is equipped with Real-
Time Executive for Multiprocessor Systems (RTEMS) as its Real Time Operating System (RTOS), on top of which
NASA’s core Flight System (cFS) is used. cFS provides a flight proven product on which OBP functions are built using open-source applications. For more information about the BrainSat architecture, refer to our co-published pro-
ceeding [12].
6. Conclusion
In this paper, we have proposed a hyperspectral imagery on-board data processing pipeline that can pave the way for fully autonomous methane detection systems using small satellites. After converting the measured raw hyperspectral data to measured at-sensor radiance, a matched filter algorithm is applied, which implies comparing the methane enhancement above background with a simulated target absorption spectrum. This result is then
fed to a Spiking Neural Network, inspired by the Hyper-STARCOP architecture but tailored to neuromorphic computers trained using a recent hybrid co-training approach.
This allows to polish the methane plume masks by reducing the false positives, which can be downlinked to mission operators for further processing. The principal bottleneck remains using a radiative transfer model on-board the platform to simulate the methane absorption spectrum according to observation parameters, which is not evaluated in this work.
The approach presented reduces the steep bandwidth requirements for hyperspectral imagers by reducing the
size of the downlinked data by a factor of ten. Limited to a maximum power usage of 4 W and a volume of 0.5 U,
the proposed hardware is ideal for CubeSats and enables several other Earth Observation applications, such as onboard cloud detection and image compression or classification.
Interesting small satellite project group appearing to be working with NASA and also have another related paper which I'm pretty has been discussed before...Brainsat.
This paper:
HERE
Who is the Space Generation Advisory Council?
Space Generation Advisory Council – In Support of the United Nations Programme on Space Applications
OUR VISION is to employ the creativity and vigour of youth in advancing humanity through the peaceful uses of space. We are the VOICE of the largest network of students, young professionals
spacegeneration.org
We are the VOICE of the largest network of students, young professionals and alumni in the space industry.
29,000+Members Globally
165+
Countries Worldwide
30+
SGAC Events Annually
11
Active Project Groups
Spiking Neural Network Design for on-board detection of methane emissions through Neuromorphic Computing
Andrew Karima∗
, Amel AlKholeifya
, Jimin Choia
, Jatin Dhalla
, Tan Hudaa
, Arnav Ranjekara
,
Yassine Yousfia
, Daniel Wischerta
a Space Generation Advisory Council, Small Satellite Project Group
* Corresponding author ( andrew.karim@spacegeneration.org andrew.karim@spacegeneration.org andrew.karim@spacegeneration.org)
Abstract
Small satellite constellations have shown tremendous success for Earth observation missions and can mimic the
performance of large satellite platforms while being cheaper and faster to deploy.
Detecting fugitive methane emissions
from ageing oil and gas infrastructures is an important use-case, as it helps facility operators to locate and mitigate these
leaks, ultimately addressing the global climate crisis. For such applications, it is crucial for the data to be transmitted
to the end-user in a timely manner for appropriate actions to take place. Edge Artificial Intelligence (AI) can address
this issue by pre-processing the data on-board the spacecraft, dramatically reducing the amount of data to be sent to
the ground and thus improving response times and bandwidth. As small satellites are greatly constrained by a low size,
weight and power (SWaP), their ability to process AI algorithms with fewer computational resources is essential.
AI applications often depend on real time data processing and analysis, yet most modern computers are inefficient in
these tasks. Neuromorphic Computing’s (NC) parallel and distributed architecture helps perform complex calculations
faster while using less power, enabling more efficient in-orbit data processing and onboard adaptive learning. This paper evaluates the potential of a novel 6U CubeSat neuromorphic on-board computer, applied to a case study for point-source methane emissions monitoring. It involves the design of a Spiking Neural Network (SNN) tailored for small satellite platforms.
The necessary processing steps for detection of point-source methane emissions using hyperspectral imagery are presented, and the potential of edge computing is discussed. A full processing pipeline enabling real-time monitoring
is proposed, which involved an SNN for segmentation of methane plumes. The architectural specifications necessary for
neuromorphic computing in the resource-constrained satellite environment are outlined. NC principles are incorporated
into the SNN architecture design, leveraging event-driven computation to improve energy efficiency and computational throughput. The processor’s performance is compared with traditional computing approaches.
Finally, a plan to train the model using an annotated dataset from the AVIRIS-NG hyperspectral instrument is presented, although training and validation are left for future work.
By embracing neuromorphic computing, an innovative approach inspired by the brain, satellites can achieve unparalleled efficiency and accuracy. The SNN model presented contributes to the scarce body of research on edge AI for greenhouse gas monitoring, providing a step forward towards real-time Earth observation.
Excerpt:
The designed OBP includes two PC104 modules, connected through a mezzanine connector. It integrates both CPU and FPGA capabilities and can cater for on board computer functions, payload data processing and down link management. The OBP is equipped with the Akida
1000 neuromorphic processor, selected for its design maturity, real-time processing capabilities and flight heritage.
The chip is tailored for event-based processing, featuring 80 Neuromorphic Processing Units (NPUs) with 100 KB
of SRAM each, supporting up to 1.2 million virtual neurons and 10 billion virtual synapses with up to 8-bit pre-
cision, ideal for inference-heavy tasks. An FPGA provides glue logic to implement necessary data protocols and serve as a soft CPU for OBC functions.
Additionally, a 12GB flight-proven Micron Solid State Device (SSD) is included to provide non-volatile on-board memory. This sub-system is constrained to a 0.5 U volume and is estimated to consume a low power of less than 4 W.
The processor’s software architecture was designed to minimize resource consumption. It is equipped with Real-
Time Executive for Multiprocessor Systems (RTEMS) as its Real Time Operating System (RTOS), on top of which
NASA’s core Flight System (cFS) is used. cFS provides a flight proven product on which OBP functions are built using open-source applications. For more information about the BrainSat architecture, refer to our co-published pro-
ceeding [12].
6. Conclusion
In this paper, we have proposed a hyperspectral imagery on-board data processing pipeline that can pave the way for fully autonomous methane detection systems using small satellites. After converting the measured raw hyperspectral data to measured at-sensor radiance, a matched filter algorithm is applied, which implies comparing the methane enhancement above background with a simulated target absorption spectrum. This result is then
fed to a Spiking Neural Network, inspired by the Hyper-STARCOP architecture but tailored to neuromorphic computers trained using a recent hybrid co-training approach.
This allows to polish the methane plume masks by reducing the false positives, which can be downlinked to mission operators for further processing. The principal bottleneck remains using a radiative transfer model on-board the platform to simulate the methane absorption spectrum according to observation parameters, which is not evaluated in this work.
The approach presented reduces the steep bandwidth requirements for hyperspectral imagers by reducing the
size of the downlinked data by a factor of ten. Limited to a maximum power usage of 4 W and a volume of 0.5 U,
the proposed hardware is ideal for CubeSats and enables several other Earth Observation applications, such as onboard cloud detection and image compression or classification.
🎙️ Thrilled to be part of the latest episode of @BrainChip 's #CES2025 All Things AI podcast! | Alan W.
🎙️ Thrilled to be part of the latest episode of @BrainChip 's #CES2025 All Things AI podcast! A big thank you to Dr. Jonathan Tapson and CMO Steve Brightfield for the insightful discussion on the future of AI in radar technology. #AI #Radar #EdgeAI #Perception
There's our mate again AKD 1000...................doing us ALL PROUD !!!!!!
SharesForBrekky
Regular
Interesting read on the concerns over Deepseek:
www.investmentmonitor.ai
"US officials are now investigating whether DeepSeek purchased NVIDIA chips through intermediaries in Singapore, effectively circumventing the AI restrictions the government had employed, Bloomberg reported."
"Nvidia’s chips, which they bought tons of, and they found their ways around it, drive their DeepSeek model […] It has got to end. If they are going to compete with us, let them compete, but stop using our tools to compete with us. So I am going to be very strong on that,” Lutnick said. If confirmed as Commerce Secretary, he would be at the helm of enforcing semiconductor restrictions."
"Typically, AI development has been understood to be very expensive and resource-intensive. Investors have expressed worry about these high costs given the sector’s slow returns. The arrival of DeepSeek and R1 has put this framework into question. After the model’s release on Monday, Nvidia’s market value decreased by nearly $600bn, dropping a staggering 17%. It was the biggest single-day loss in the history of the US stock market."
Effectiveness of US AI restrictions questioned after DeepSeek upset
US officials are now investigating whether DeepSeek obtained Nvidia chips through intermediaries in Singapore to avoid AI restrictions.
"US officials are now investigating whether DeepSeek purchased NVIDIA chips through intermediaries in Singapore, effectively circumventing the AI restrictions the government had employed, Bloomberg reported."
"Nvidia’s chips, which they bought tons of, and they found their ways around it, drive their DeepSeek model […] It has got to end. If they are going to compete with us, let them compete, but stop using our tools to compete with us. So I am going to be very strong on that,” Lutnick said. If confirmed as Commerce Secretary, he would be at the helm of enforcing semiconductor restrictions."
"Typically, AI development has been understood to be very expensive and resource-intensive. Investors have expressed worry about these high costs given the sector’s slow returns. The arrival of DeepSeek and R1 has put this framework into question. After the model’s release on Monday, Nvidia’s market value decreased by nearly $600bn, dropping a staggering 17%. It was the biggest single-day loss in the history of the US stock market."
Wondering if BrainChip management are still claiming a three year lead over its competitors.Interesting read on the concerns over Deepseek:
Effectiveness of US AI restrictions questioned after DeepSeek upset
US officials are now investigating whether DeepSeek obtained Nvidia chips through intermediaries in Singapore to avoid AI restrictions.
"US officials are now investigating whether DeepSeek purchased NVIDIA chips through intermediaries in Singapore, effectively circumventing the AI restrictions the government had employed, Bloomberg reported."
"Nvidia’s chips, which they bought tons of, and they found their ways around it, drive their DeepSeek model […] It has got to end. If they are going to compete with us, let them compete, but stop using our tools to compete with us. So I am going to be very strong on that,” Lutnick said. If confirmed as Commerce Secretary, he would be at the helm of enforcing semiconductor restrictions."
"Typically, AI development has been understood to be very expensive and resource-intensive. Investors have expressed worry about these high costs given the sector’s slow returns. The arrival of DeepSeek and R1 has put this framework into question. After the model’s release on Monday, Nvidia’s market value decreased by nearly $600bn, dropping a staggering 17%. It was the biggest single-day loss in the history of the US stock market."
TheUnfairAdvantage
Regular
Sally Ward Foxton wrote an article Liked by Rob Telson on LinkedIn. Article is headlined “Top 10 edge AI chips. Now, I thought SWP being a friend of Brainchip and with her vast interactions with Brainchip, would know where Akida fits into the hierarchy of edge chip technology! Evidently according to SWF, Akida doesn’t rank in the top 10. Am I missing something?
https://www.electronicproducts.com/top-10-edge-ai-chips/
https://www.electronicproducts.com/top-10-edge-ai-chips/
Don't know about that but Brainchip and its cloistered management are making me feel 19 again.
19 cents that is.
Smoothsailing
Regular
One can only hope we are the secret sauce in one of these companies as it is certainly hard to digest why we’re not mentioned
Tony Lewis posted that DeepSeek is a boom for us.
Made a lot of points.
The post has been posted earlier here.
Can't remember details but it was very positive.
Smoothsailing
Regular
Ask Chat GPT
Neuromorphic Earpieces Vendors
Neuromorphic earpieces are advanced devices that utilize neuromorphic engineering principles to interface with the human brain and body. These earpieces can detect brain waves, muscle impulses, and other physiological signals to facilitate various applications, including wellness, communication, and control of devices.
Neuromorphic Earpieces Vendors
Neuromorphic earpieces are advanced devices that utilize neuromorphic engineering principles to interface with the human brain and body. These earpieces can detect brain waves, muscle impulses, and other physiological signals to facilitate various applications, including wellness, communication, and control of devices.
- NeuroSky: NeuroSky is a prominent company known for its biosensor technologies that provide insights into health and wellness through wearable devices. They offer products such as the MindWave Mobile EEG headset, which can be paired with applications that utilize brainwave data for learning effectiveness and interactive experiences.
- Neuvana: Neuvana produces the Xen device, which includes specialized earbuds designed to stimulate the vagus nerve through gentle electrical signals. This technology aims to enhance relaxation and improve overall wellness by leveraging neurotechnology in a user-friendly format.
- Naqi: Naqi has developed an innovative earbud platform that detects brain waves and muscle impulses to create a hands-free interface for controlling various devices. Their technology allows users to interact with computers or simulators without traditional input methods.
Smoothsailing
Regular
2. BrainChip BrainChip is a neuromorphic computing company that has developed the Akida chip, which supports both SNNs and convolutional neural networks (CNNs). The Akida chip is designed for edge AI applications and features a highly efficient architecture that allows it to perform tasks like embedded vision and audio processing while consuming minimal power.Are we not an IP company? Those companies produce chips. Maybe that’s why we’re not on the list
Super sleutiing FMF!
M Fouda's linkedin confirms the RainAi connexion:
https://www.linkedin.com/in/mefouda/recent-activity/all/
There is also a repost originally from Prof Salama about the article.
A great thing about the diagram is that it illustrates the synergistic relationship between Akida and Edge Impulse in creating the model.
Anothet thing the article shows is that Akida's agnosticism extends beyond processors to DVS cameras, including Prophesee's arch rival Inivation.
Last edited:
感情検出・認識（EDR）：市場シェア分析、産業動向と統計、成長予測（2025年～2030年）
感情検出・認識（EDR）市場規模は、2025年に684億1,000万米ドルと推定され、予測期間（2025-2030年）のCAGRは19.49%で、2030年には1,666億3,000万米ドルに達すると予測されます。
www.gii.co.jp
Need to translate to English and search brainchip
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