BRN Discussion Ongoing

[Frangipani]

Can't recall if this recent presentation paper at MWSCAS has been posted (probs) but thought put it up as couldn't be bothered doing a search.

From a group out at Dayton Uni and nice results for Akida against Loihi.

MWSCAS 2025

www.mwscas2025.org

13:30-13:45, Paper TueLecB04.1
Ultra-Efficient Network Intrusion Detection Implemented on Spiking Neural Network Hardware (I)
Islam, Rashedul	University of Dayton
Yakopcic, Chris	University of Dayton
Rahman, Nayim	University of Dayton
Alam, Shahanur	University of Dayton
Taha, Tarek	University of Dayton
Keywords: Neuromorphic System Algorithms and Applications, Machine Learning at the Edge, Other Neural and Neuromorphic Circuits and Systems Topics Abstract: Network intrusion detection is crucial for securing data transmission against cyber threats. Traditional anomaly detection systems use computationally intensive models, with CPUs and GPUs consuming excessive power during training and testing. Such systems are impractical for battery-operated devices and IoT sensors, which require low-power solutions. As energy efficiency becomes a key concern, analyzing network intrusion datasets on low-power hardware is vital. This paper implements a low-power anomaly detection system on Intel’s Loihi and Brainchip’s Akida neuromorphic processors. The model was trained on a CPU, with weights deployed on the processors. Three experiments—binary classification, attack class classification, and attack type classification—are conducted. We achieved approximately 98.1% accuracy on Akida and 94% on Loihi in all experiments while consuming just 3 to 6 microjoules per inference. Also, a comparative analysis with the Raspberry Pi 3 and Asus Tinker Board is performed. To the best of our knowledge, this is the first performance analysis of low power anomaly detection based on spiking neural network hardware.

Just like a few weeks ago, when you yourself had posted about that same conference paper, I’m going to reply with this:


https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-470870

 

[Frangipani]

Posters who love to diss our competition will surely say “But… they’re not digital… they don’t have on-chip learning… they’re only good enough for harvesting the low-hanging fruit etc.”, yet, when they really open their eyes, they cannot deny that the team behind Delft-based Innatera are making strides in targeting those low-hanging fruit (which are also part of BrainChip’s market!) and will have to acknowledge that Innatera are very present both on social media and at real-life events (which - at the same time - is good for us, as it helps to spread awareness of the benefits of neuromorphic computing in general).

Some recent examples:

Innatera’s Pulsar microcontroller shines at SEMICON India | Shreyas Derashri

Thrilled to be heading into SEMICON India 2025 (SEMI India) this week 🇮🇳🇳🇱! This event is a milestone for us at Innatera — we are showcasing live use cases built by our partners on our neuromorphic processor. Seeing partners like Aaroh Labs (smart smoke detection) and CYRAN AI Solutions...

www.linkedin.com

#risewithaarohlabs #samiconindia #eaglesmokedetector #launch | Aaroh Labs

𝐈𝐧𝐯𝐢𝐭𝐚𝐭𝐢𝐨𝐧 𝐭𝐨 𝐀𝐚𝐫𝐨𝐡 𝐋𝐚𝐛𝐬' 𝐒𝐦𝐨𝐤𝐞 𝐃𝐞𝐭𝐞𝐜𝐭𝐨𝐫 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐋𝐚𝐮𝐧𝐜𝐡 You're invited to join us at Aaroh Labs as we unveil our innovative Smoke Detector product, powered by Innatera' s cutting-edge Spiking Neural Processor – a brain-inspired architecture that brings ultra-low power consumption...

www.linkedin.com

Innatera’s Pulsar microcontroller shines at SEMICON India

Breakthrough neuromorphic chip brings real-time, ultra-low-power intelligence to next-gen sensing applications

innatera.com

The founder of Innatera partner CYRAN AI Solutions, Manan Suri, is in turn closely connected to TCS Research - he has been a Research Advisor to the TCS Innovation Team on Neuromorphic Computing and Edge AI since June 2021…

The below LinkedIn post is another reminder that our friends at TCS Research - Sounak Dey, Arijit Mukherjee & Arpan Pal - are not exclusively friends with us, when it comes to neuromorphic computing, but also close friends with others developing their own technology in that field, eg. Manan Suri and his research group at IIT Delhi. In 2018, Manan Suri founded CYRAN AI Solutions as a spinoff from that uni lab and has been a Research Advisor to the TCS Innovation Team on Neuromorphic Computing and Edge AI since June 2021.

#indian #industry #deeptech #academia #intelligent #neuromorphic #intelligence #space #sensor #imc | Manan Suri | 12 comments

Common discourse goes- "#Indian #Industry is often not investing in #deeptech R&D while #Academia stays away from ground requirements of the industry" There are increasing number of exceptions to this chatter 🙂! Here, I'll highlight one such close collaboration b/w our research group (at...

www.linkedin.com

View attachment 82693 View attachment 82694

Manan Suri - Indian Institute of Technology, Delhi | LinkedIn

| MIT - TR 35 | IEEE EDS Early Career | Young Scientist NASI | Young Engineer IEI |… · Experience: Indian Institute of Technology, Delhi · Education: Institut national polytechnique de Grenoble · Location: India · 500+ connections on LinkedIn. View Manan Suri’s profile on LinkedIn, a...

www.linkedin.com

View attachment 82699

View attachment 82700

This Engineer’s Hardware Is Inspired by the Brain

Manan Suri’s neuromorphic systems run AI on sensors, drones, and VR headsets

spectrum.ieee.org

This Engineer’s Hardware Is Inspired by the Brain​

Manan Suri’s neuromorphic systems run AI on sensors, drones, and VR headsets​

EDD GENT
23 JAN 2024
5 MIN READ

Manan Suri displays a wafer-level testing system for unpackaged chips and devices built by researchers at the Indian Institute of Technology Delhi.
MANAN SURI


In work and in life, it’s easy to get stuck in your ways. That’s why Manan Surihas always looked to expand his horizons both professionally and personally.
Growing up in India, he was used to transitions and new experiences because his family frequently moved around the country as his father relocated for his job as a chemical engineer. Traveling stuck with Suri in adulthood. He studied and worked in Dubai, the United States, France, and Belgium over the course of his twenties.

Manan Suri​

EMPLOYER:
Indian Institute of Technology Delhi
OCCUPATION:
Associate professor and founder of Cyran AI Solutions, New Delhi
EDUCATION:
Bachelor’s and master’s degrees in electrical and computer engineering, both from Cornell; Ph.D. in nanoelectronics from the CEA-Leti research institute in Grenoble, France

Eventually, Suri moved back to India to become an assistant professor at the Indian Institute of Technology Delhi. There he set up a research group focused on developing brain-inspired (neuromorphic) computer hardware for low-power devices like sensors, drones, and virtual-reality headsets. He is now an associate professor.

He also launched a startup to commercialize his lab’s expertise: Cyran AI Solutions, based in New Delhi, works with companies and government agencies on a variety of projects. These include automating the inspection process for identifying defects in semiconductors and developing computer-vision systems to improve crop yields and analyze geospatial Earth-observation data.
While balancing a career in academia and industry is challenging, Suri says, he relishes the opportunity to constantly learn.

“Once I’ve figured out how a system works, I start getting bored,” he says.
Suri, an IEEE member, believes that embracing change is a key ingredient for success. This is what has driven him to continually move on to new projects, push into new disciplines, and even move from country to country to experience a different way of life.

“It accelerates your ability to learn new things,” he says. “It puts you on a fast trajectory and helps shed some of your inhibitions or get over the inertia in what you’re doing or how you’re living.”

Inspired by Cornell’s semiconductor lab​

Growing up, Suri’s passion was physics, but he quickly realized he was drawn more to the practical applications than theory. This led to a fascination with electronics.

In 2005 he initially enrolled at the Birla Institute of Technology and Science, Pilani, in India, and studied electronics and instrumentation at the institute’s campus in Dubai. After his second year, he transferred to Cornell, in Ithaca, N.Y. His first six months living in the United States, acclimating to a new culture and a different academic environment, were overwhelming, Suri says. What hooked him were Cornell’s high-end facilities available to students studying semiconductor engineering and nanofabrication—in particular, the industry-grade semiconductor clean rooms.

He earned a bachelor’s degree in electrical and computer engineering in 2009 and a master’s degree in the same subject the following year.

New skills in computational neuroscience​

After graduating, Suri received offers for Ph.D. positions in the United States and Europe to work on conventional electronics projects. But he didn’t want to get pigeonholed as a traditional semiconductor engineer. He was intrigued by an offer to study neuromorphic systems at the CEA-Leti research institute in Grenoble, France. He was also eager to broaden his life experience and get a taste of the European way of doing things.

The work would push Suri to develop new skills in computational neuroscience and computer science. In 2010 he started a Ph.D. program in the institute’s Advanced Memory Technology Group. There he worked on low-power AI hardware that uses new kinds of nonvolatile memory to emulate how biological synapses process data. This involved using phase-change memory and conductive-bridging RAM to create neural networks for visual pattern extraction and auditory pattern sensitivity.

Suri discovered that his experience with electronics allowed him to approach neuromorphic engineering problems from an entirely different angle than his colleagues had considered. Experts can develop fairly rigid and conventional ways of thinking about their own field, he says, but when those with different skill sets apply them to the same problems, it can often lead to more innovative thinking. “You bring a completely different perspective,” he says. “It leads to a lot of creativity.”

Setting up his own research lab​

After finishing his doctorate in nanoelectronics, Suri got a job working on high-voltage transistors for automotive applications at the semiconductor designer NXP Semiconductors, in Brussels. Since his role was to take a project all the way from concept to fabrication, it was as close to pure research as he could get in industry. But as interesting as the work was, Suri says, he missed the intellectual freedom of academia.

When the opportunity of setting up his own lab at IIT Delhi came along, he jumped at it. He had also been away from his home country for almost a decade and wanted to be closer to family and contribute to the Indian science and technology ecosystem, he says.
“Moving abroad was more a matter of collecting experiences and seeing how different places work,” he says.

Suri’s group at IIT Delhi has made contributions to AI hardware, neuromorphic hardware, and hardware security. The group collaborates with industry research teams around the world, including Meta Reality Labs, Tata Consultancy Services, and GlobalFoundries.

​

Launching a startup​

Despite returning to academia, Suri says he has always been interested in developing practical solutions to real-world challenges, and this goal has guided his research. Whatever project he works on, he always asks himself two questions: Will it solve a real problem? And will someone buy it?

Suri launched his startup in 2018 to turn some of his lab’s work in AI and neuromorphic hardware into commercial products. Cyran AI Solutions’ customers hire the company to solve a range of problems. These have included computer-vision systems for detecting defects in computer chips; hyperspectral data-analysis algorithms designed to run in real time on chips for crop-inspection drones; and AI systems for small, low-power devices and challenging environments like satellites.

Manan Suri and researchers at the Indian Institute of Technology Delhi’s lab designed this custom electrical test setup for the characterization of memory-computing chips. MANAN SURI

While Cyran makes use of its neuromorphic expertise for some problems, it often uses more mature and simpler-to-deploy machine-learning approaches.

“Most users don’t really care about what technology we are using,” Suri says. “They just want functional performance at the most cost-effective price.”

One of the biggest lessons Suri learned from running a startup is to consider the market being served. For earlier projects, he says, the company often devised a solution that was specific to just one customer’s needs and couldn’t be repurposed for other uses. To create a sustainable business, he realized he needed to develop generic solutions that could be deployed more broadly.

“Running Cyran has been like [pursuing a] mini-MBA,” he says. “You need to really pay attention to the market aspects and not just the technology.”

In 2018, MIT Technology Review named Suri one of its 35 Innovators Under 35for his work on neuromorphic computing.

The need to be hands-on​

Keeping a foot in both academia and industry can be challenging, Suri says. Facing resource crunches, whether in time, staffing, or funding, is common. The only way he’s able to manage things is to plan extensively and remain nimble, building in contingencies.

If you can manage it, Suri says, having your fingers in many pies can have major benefits. In particular, working on problems that bridge several disciplines can help you break out of rigid thinking and come up with novel solutions.

It’s not possible to dedicate equal amounts of time to learning every area, he says, so he advises up-and-coming engineers to carefully pick the topics that are most likely to advance their progress. It’s also crucial to dive in and get your hands dirty, rather than focusing on theory, initially.

“Take the plunge and try and figure it out,” he recommends. “As the problem unravels, then you can start getting into the theory or the more formal aspects of the project. You also start to appreciate learning more about the theory as it gets more hands-on.”

https://www.cyran.in/

View attachment 82698

… and we know that Sounak Dey from TCS Research does not have Akida-only blinders on when it comes to neuromorphic computing:


https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-438883

Also cf. this June 2025 paper:

Despite our years of collaboration with TCS researchers and the above encouraging affirmation of an “active alliance” with Tata Elxsi, we should not ignore that TCS are also exploring other neuromorphic options for what will ultimately be their own clients.

And while a number of TCS patents do refer to Akida as an example of a neuromorphic processor that could be utilised, they always also refer to Loihi, as far as I’m aware of.

A recent case in point for Tata Consultancy Research’s polyamory is the June 2025 paper “The Promise of Spiking Neural Networks for Ubiquitous Computing: A Survey and New Perspectives”, co-authored by five Singapore Management University (SMU) researchers as well as Sounak Dey and Arpan Pal from TCS, both very familiar names to regular readers of this forum.

Although we know those two TCS researchers to be fans of Akida, they sadly did not express a preference for BrainChip’s neuromorphic processor over those from our competitors in below paper published less than six weeks ago.
On the contrary, in their concluding “key takeaway” recommendations of neuromorphic hardware (“We make the following recommendations for readers with different needs considering neuromorphic hardware chipsets”), the seven co-authors do not even mention Akida at all.

Even more surprisingly, the section on Akida is factually incorrect:
- AKD1500 is a first generation reference chip and is not based on Akida 2.0, BrainChip’s second generation platform that supports TENNs and vision transformers.
- An AKD2000 reference chip does not (yet) exist - it may or may not materialise. At present, only Akida 2.0 IP is commercially available - not an actual silicon chip, as claimed by the paper’s authors.
- The paper is in total ignorance of ultra-low-power Akida Pico, operating on less than 1mW of power, which was revealed by our company back in October 2024 and is based on Akida 2.0.
It is highly unlikely this (possibly revised) version of the paper published on 1 June 2025 would have been submitted to arXiv prior to BrainChip’s announcement of Akida Pico, and we can safely assume Sounak Dey and Arpan Pal would have been aware of that October 2024 BrainChip announcement (unlike maybe their SMU co-authors).

One could argue the reason Akida Pico is not mentioned could possibly be that an actual Akida Pico chip is not commercially available, yet, given the authors state

“5.2 Neuromorphic Hardware
In this subsection, we summarize the latest commercially available neuromorphic hardware chipsets, highlighting their capabilities and development support for building and deploying spiking neural networks.”,

which, however, in turn begs the question, why Loihi 2 is listed, then, as it was always conceptualised as a research chip and is not commercially available. In the paragraph on Loihi 2, the authors correctly state that “this neuromorphic research chipset is available only through the Intel Neuromorphic Research Community (INRC).”

Given the fact that Sounak Dey and Arpan Pal co-authored this paper, the above inaccuracies are bewildering, to say the least. Did the two TCS researchers who both have firsthand experience with Akida contribute to only part of this paper and not proofread the final version before it was submitted?

Either way not a good look…

https://arxiv.org/pdf/2506.01737v1

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(…)

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Socionext Europe • What happens when precision radar sensor meets brain-inspired AI?

At Socionext, we’re excited to partner with Innatera to unlock the next generation of smart automation. In our latest video, Miguel Estevez, Product Marketing…

www.eu.socionext.com

Then there was this video by Anastasiia Nosova in her “Anastasi In Tech” YouTube channel a few weeks ago:

Interesting !!

In recent months, Innatera have also been really chummy with Pete Bernard and the Edge AI Foundation team:

#edgeai #genedgeai #agenticedgeai #neuromorphic #tinyml #edgeaievents | Pete Bernard

Yes, "I am presenting" - see you in Amsterdam, in late September. The EDGE AI FOUNDATION has coordinated, for the first time, with Wienke Giezeman and his team to bring the edge AI community to The Things Conference. We will have over a dozen partners there, including Arduino, Edge Impulse (a...

www.linkedin.com

 

[Frangipani]

From biology to business: Neuromorphic computing pathways to intelligent innovation

From algorithms to AI: Promise, power, and the pressing challenges ahead Large scale datasets and in...

www.finextra.com

From biology to business: Neuromorphic computing pathways to intelligent innovation​

3 hours Be the first to comment

Contributed​

This content is contributed or sourced from third parties but has been subject to Finextra editorial review


From algorithms to AI: Promise, power, and the pressing challenges ahead
Large scale datasets and information processing requirements, within complex environments, are continuously reaching unprecedented levels of sophistication, especially in the advent of artificial intelligence and other emerging technologies, pushing the boundaries of fierce competition for available, scalable and adaptable computing resources.

The magnitude of convoluted problems and the complexity of workloads require novel computing approaches that can perform multiplex computational tasks in a speedy, effective, interoperable, cost-effective and energy-efficient manner, along with, being resilient against cyber threats and attacks. At the same time, the wide variety of these workloads is driving the need for multiple compute options that are able to handle such kind of data-intensive and high-performance operations.

Global data centre infrastructure (excluding IT hardware) capital expenditures are expected to exceed $1.7 trillion by 2030. Moreover, $1 trillion worth of data centres would need to be built in the next several years to support the deployment of generative AI capabilities. Data centres and transmission networks are deemed as responsible for the 1% of global energy-related greenhouse gases. The electricity consumed by data centres globally can more than double by 2026 to more than 1,000 TWhs. Continuous advancements in artificial intelligence will result in data centres end up using 4.5% of globally-generated energy by 2030. Since 2017, global electricity usage by data centres has grown by around 12% annually, more than four times the rate of overall electricity consumption. This new reality projects a data and computational resources tetralemma that is branched across four crucial elements: decarbonisation, affordability, accessibility, and reliability.

Groundbreaking innovations in the design of machines and algorithms have opened new doors for integrating fundamental principles of biology, humanities, neuroscience, and cognitive science, towards the development of human-like artificial intelligence.


When computers think like neurons: The rise of neuromorphic technology

Neuromorphic computing, as an emerging computing engineering concept, receives inspiration from biology and more specifically from the architecture and functioning of the complexity of the biological brain. The human brain, with less than 20 Watts of power consumption and approximately 1000 trillion operations per second, is and remains the most complex, efficient and powerful known structure within our world. The human brain magically outperforms state-of-the art supercomputers in terms of energy and volume, making it a more versatile, a more energy-efficient and a more adaptable information processor. By being positioned in the biology and neuroscience, mathematics, and physics, computer science and electronic engineering nexus, neuromorphic computing envisions to lead a new and exciting chapter in computational growth, beyond the physical limitations of Moore’s Law, the observation, made by Gordon Moore in 1965. According to Moore’s Law, the number of transistors on an integrated circuit (and thus computing power) tends to double approximately every two years, while the cost per transistor decreases. This trend has driven rapid growth in computing performance, however it has also created a major challenge: keeping up requires making transistors smaller and more complex, which is becoming harder, more expensive, and closer to physical limits.

Neuromorphic computing endeavours to emulate the human brain's processing capabilities. By mimicking the structure and operation of biological neural networks, neuromorphic computing aims at replicating the human brain’s, efficiency, adaptability, synaptic plasticity, and learning capabilities, with calculations being performed directly in the memory.

Neuromorphic computing systems use electronic circuits to simulate neurons and synapses, enabling them to process information in ways that are fundamentally different from traditional computers. This parallel processing of data ensures enhanced performance and energy efficiency.

This technological advancement of the next generation of computing becomes crucial because it also enriches our understanding of the brain and cognition, while at the same time it allows for further innovations towards the development of ultra-low power cognitive computing systems. In the future, low-power devices (e.g. smartphones, IoT sensors) will highly likely be able to run powerful AI models, enabling on-the-edge-computing, while gradually reducing current dependencies on cloud resources.

Neuromorphic computing systems can learn from their environment, adapt to changes, and use event-driven processing, improving their performance over time through mechanisms such as synaptic plasticity. The ability to learn and adapt, results in the development of more sophisticated, capable, adaptive and contextual artificial intelligence systems with significant advancements in pattern recognition, autonomous decision making and real-time processing of various events, especially when conditions become unpredictable or variable.


When brains meet machines: How neuromorphic computing could reshape industries

As an emerging technology, neuromorphic computing is considered as a critical enabler within potential business applications and use cases across various industries. In the era of artificial intelligence and machine learning, neuromorphic computing can contribute to optimal performance of deep learning tasks, allowing for the deployment of computer vision and sophisticated natural language processing capabilities. Such kind of tasks require the processing of vast amounts of data at high speeds and the analysis of complex data in real-time for more informed decision making.

Within the field of financial services and capital markets, indicative use cases of neuromorphic include large and complex financial data analysis and processing, fraud detection (neuromorphic-based anomaly detection systems for transaction patterns, hidden correlations and inconsistencies and customer data analysis), risk assessment and evaluation (enhanced by a combination of neuromorphic computing and machine learning for better and accurate precision), optimisation of trading methodologies (via potential synergies between intelligent neuromorphic computing and advanced machine learning algorithms) especially in times of financial market volatility, real-time trading decision-making and anomaly detection in markets, all contributing to financial security and encouraging of sustainable practices. Within financial services and capital markets, an integration of intelligent neuromorphic computing capabilities with machine learning algorithms can potentially lead to robust systems for processing and analysing complex and vast patterns in data, ensuring enhanced precision, scalability, delivery of highly personalized services (e.g. investment advice, loan products, financial planning) and flexibility of financial applications, towards achieving customer satisfaction and loyalty.

Other indicative neuromorphic computing applications include smart vision sensors, speech and image processing, myoelectric prosthetics limbs and control, wearable healthcare systems and computational electronic skin (e-skin), gesture control applications (smart home devices, offices, factories), autonomous and near-human touch sensitive robots, self-driving vehicles and drones.

All these applications showcase that machines require greater adaptability to perform complex tasks by processing sensor information in real-time and making autonomous decisions by continuously learning from their environments. This means that in the future users will be able to interact with computers in more human-like, immersive virtual and augmented reality ways.


Shaping the future: Why business leaders need to pay attention to neuromorphic computing

Discussing neuromorphic computing in business context, goes beyond how computers are actually being designed and used. It is really all about acknowledging and understanding that our ongoing relationship with technology is reshaped and redefined, especially in light of the energy-related and physical limitations posed by traditional computing technologies.

Business leaders need to start thinking strategically in terms of understanding the applications and the anticipated transformative impact of neuromorphic computing by appreciating these upcoming changes, the opportunities for multidisciplinary innovation, the iterative small-scale experimentations and the mobilisation of strategic preparedness and investments. Emerging applications, such as brain-machine interfaces, bioinformatics and neuromorphic chips in IoT devices, pave the way towards a new era of computational innovations.

Businesses need to ensure that they will be fit-for-purpose in terms of maintaining a transient advantage within the future world of computing, along with, reducing their environmental footprints in alignment to their sustainability goals and decarbonisation/net-zero endeavours.

It is critical for business leaders to stay informed about these technological advancements, invest in dedicated neuromorphic computing R&D and consider strategic partnerships with tech companies, neuromorphic chip developers (e.g. Loihi by Intel Lab, TrueNorth by IBM, Akida by BrainChip), universities and research institutions, in order to access cutting-edge neuromorphic computing capabilities, hardware, algorithms and talent.

Boards of directors need to start asking the right questions and be prepared to lead the way as the technology matures, by thinking and answering important ethical, societal, economic, and governance questions, especially in relation to data governance, privacy concerns (gathering, storing and processing private information) and mitigation of bias (ensuring accuracy of data analysis, explainability of outcomes/recommendations and detection of bias in machine learning algorithms and data used for training purposes).

Neuromorphic computing allows us to get a glimpse into the future where machines can think and learn in ways that are more human-like compared to traditional computers, offering challenging but promising avenues towards developing the next generation of intelligent large-scale, accessible, reliable, and energy-efficient computing systems.

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Dimitrios Salampasis
Assoc. Professor, Emerging Technologies & FinTech
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Dr Dimitrios Salampasis (PhD, FCSI, MAICD, MSID, CMBE) - Swinburne University of Technology | LinkedIn

A values-driven, award-winning, and visionary global leader, passionate about innovation… · Experience: Swinburne University of Technology · Education: Harvard Business School Online · Location: Melbourne · 500+ connections on LinkedIn. View Dr Dimitrios Salampasis (PhD, FCSI, MAICD, MSID...

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[Guzzi62]

Slightly off-topic, but I found the video very interesting and thought some here might as well.

How to make a TSMC fab in Arizona.

They can "only" make 4&5nm chips there, 2nm is reserved for Taiwan for likely mostly geopolitical reasons.

 

[stockduck]

Is our Vice President of Sales connected to verizon? I don`t get it...because of some email adresses.

"https: //rocketreach.co/steve-thorne-email_82456835"

Could be nothing.

 

[7für7]

Is our Vice President of Sales connected to verizon? I don`t get it...because of some email adresses.

"https: //rocketreach.co/steve-thorne-email_82456835"

Could be nothing.

Most likely that’s just a private @verizon.net email address. In the US many people use Verizon, Comcast, AT&T etc. as their personal providers …RocketReach often pulls those into its listings. Officially, Steve Thorne is VP of Sales at BrainChip, and there’s no evidence of any business link to Verizon. I also couldn’t find any hint on LinkedIn

 

[Pom down under]

As we were up 7% yesterday I’m guessing we will drop 5% today

 

[AARONASX]

Slightly off-topic, but I found the video very interesting and thought some here might as well.

How to make a TSMC fab in Arizona.

They can "only" make 4&5nm chips there, 2nm is reserved for Taiwan for likely mostly geopolitical reasons.

you'll enjoy this one also

 

[7für7]

As we were up 7% yesterday I’m guessing we will drop 5% today

 

[Labsy]

So my baseless, uneducated guess for the current price hike is that it is either a manipulated pump to possibly 24 cents or some inside knowledge on Mercedes release... In which case it will hit 3 dollars. Let's see what it looks like this time next week. Have a good weekend chippers.

 

[7für7]

Brainchip related article

https://www.allaboutcircuits.com/news/brainchips-cloud-platform-lets-developers-to-use-its-neuromorphic-tech/

 

[7für7]

So my baseless, uneducated guess for the current price hike is that it is either a manipulated pump to possibly 24 cents or some inside knowledge on Mercedes release... In which case it will hit 3 dollars. Let's see what it looks like this time next week. Have a good weekend chippers.

Not sure about that… looks more like going back to 20,5… 21 …

WOHOOOOOOO

 

[MDhere]

Not sure about that… looks more like going back to 20,5… 21 …

WOHOOOOOOO

I am really not sure how you get that assumption when there are substantial buys probably covering for the weekend in lead up to whatever is around the corner. But it may be one of your jokes which I never quite get.. I am gathering it is one of those.

 

[7für7]

Just as a closing note… I’ve often said that I see all the other partnerships as more promising than always clinging to Mercedes – at least in the long run. And the back-and-forth in the comments from Mercedes’ side… just say no, or ‘not planned at the moment,’ or whatever, instead of making a mystery out of it. Totally overblown in my opinion. DYOR

 

[7für7]

I am really not sure how you get that assumption when there are substantial buys probably covering for the weekend in lead up to whatever is around the corner. But it may be one of your jokes which I never quite get.. I am gathering it is one of those.

Don’t take everything so serious…I don’t have insight into the ordebooks.. I just see when it goes down to 21,8… that’s all… it’s a running gag meanwhile…

Edit 21.5 now.. as I said I just see the current movement and not the buy orders ..

It’s interesting that my factual posts are always ignored and hardly noticed, and then later someone—probably one of those who ignore me—reposts it as their own and suddenly gets 2,000 likes. Doesn’t bother me… I’m fine with the few people who understand my humor and at least acknowledge my factual posts. What annoys me is when someone immediately comments on my nonsense fun posts out of boredom, while staying completely silent on my serious ones. If you keep out of my factual posts, then please stay out of my nonsense posts as well. Thanks.

 

[Pom down under]

So my baseless, uneducated guess for the current price hike is that it is either a manipulated pump to possibly 24 cents or some inside knowledge on Mercedes release... In which case it will hit 3 dollars. Let's see what it looks like this time next week. Have a good weekend chippers.

Maybe this is where Sean comes out with an IP license agreement worth $9 million

 

[TheUnfairAdvantage]

I just heard that someone I’ve had on ignore for years wrote an essay about me. Thank you.

An essay. That’s too much effort for your contribution. I heard it was a simple limerick. Here it is in case you missed it.

There was once an odd fellow named Slade.

Put a bet on BRN and thought he would get paid.

And the end of the race, it couldn’t even manage third place.

For now it is Tooheys for poor Slade. Not even enough Thai Baht left to get laid.

 

[MDhere]

An essay. That’s too much effort for your contribution. I heard it was a simple limerick. Here it is in case you missed it.

There was once an odd fellow named Slade.

Put a bet on BRN and thought he would get paid.

And the end of the race, it couldn’t even manage third place.

For now it is Tooheys for poor Slade. Not even enough Thai Baht left to get laid.

I think Slade has enough for a few Singha beers though

 

[DingoBorat]

Not sure about that… looks more like going back to 20,5… 21 …

WOHOOOOOOO

There's always cash term deposits.

 

[Fullmoonfever]

Appears new job advertised last couple of days.

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Senior Product Manager​

BrainChip Laguna Hills, CA
2 days ago 76 applicants​

We are seeking a technically proficient Senior Product Manager to lead the evolution of BrainChip’s Akida™ platform – our ultra-low power, event-based neuromorphic processor IP. This role is not about marketing collateral; it’s about shaping the future of edge AI with real product leadership. You will drive end-to-end product lifecycle execution, from early-stage discovery and requirements definition to cross-functional delivery and iterative refinement.



The ideal candidate has a deep technical background (e.g., in semiconductors, edge computing, or embedded AI), but is equally comfortable translating complex ideas into product plans, business cases, and discussions with non-technical stakeholders.



This is a hybrid role and is required in our Laguna Hills, CA office 3 days a week.



Key Responsibilities

• Own the product lifecycle from concept through end-of-life for a portfolio that includes:
• AI IP cores
• Neuromorphic model libraries
• Development kits
• Edge AI hardware systems (SoCs, silicon, and evaluation boards)
• Author detailed and technically sound Product Requirement Documents (PRDs) to drive software, silicon, and model development roadmaps.
• Partner with engineering using Agile practices to scope, prioritize, and deliver neural model features, hardware improvements, and developer tooling.
• Develop strong market and technical perspectives to guide trade-off decisions in architecture, performance, and time-to-market.
• Create, maintain, and refine business cases that span technical viability, market readiness, financial return, and ecosystem integration.
• Collaborate cross-functionally with engineering, business development, architecture, and executive leadership to ensure product alignment and delivery.
• Support strategic product planning by engaging in industry standards efforts, customer conversations, and competitive landscape analysis.
• Contribute to the ongoing expansion of BrainChip’s future product pipeline with a balance of technical rigor and market insight.

Required Qualifications

Education:

• Bachelor's degree in Electrical Engineering, Computer Engineering, or related technical discipline
• MBA or Master’s in Management/Technology preferred

Experience:

• 5–10 years in product management or technical product leadership in semiconductors, embedded AI, or edge computing
• Proven experience writing technical PRDs and collaborating deeply with hardware/software engineering teams
• Solid understanding of edge AI/ML infrastructure, AI inference workloads, IP blocks, and model deployment pipelines
• Familiarity with neuromorphic computing or event-based processing is a strong plus
• Experience working with developer ecosystems, SDKs, and AI model optimization tools (e.g., TensorFlow, PyTorch, MetaTF)

Skills & Attributes

• Highly technical yet an excellent communicator with both engineers and executives
• Strategic thinker with a bias for execution
• Proficient in Agile and product development workflows
• Skilled in market discovery, competitive analysis, and creating compelling value propositions
• Comfortable navigating ambiguity and shaping product strategy in a fast-paced, innovation-driven environment
• Driven by curiosity, resilience, and the ability to learn on the fly

We are not looking for a generalist product marketer. We need a technical product strategist, a doer and thinker who wants to be part of something fundamentally new and transformative.



Apply now to help define the next generation of intelligent computing.