BRN Discussion Ongoing

[Gazzafish]

Good 4c in my opinion

 

[TheDrooben]

🚀 Data-Backed Prediction: Neuromorphic Computing Will Transform Energy-Efficient AI in Enterprises—Sooner Than Expected | Mohamed Alhashme, PhD

🚀 Data-Backed Prediction: Neuromorphic Computing Will Transform Energy-Efficient AI in Enterprises—Sooner Than Expected A couple months ago, I reviewed energy forecasts in a leading industry report, and the figures were daunting: AI power demands could double, straining budgets and...

www.linkedin.com

Happy as Larry

 

[itsol4605]

Not Brainchip Akida ... but a good sign

In a world of kilowatt GPUs, a sub-milliwatt AI chips is a novelty. | Joseph B.

In a world of kilowatt GPUs, a sub-milliwatt AI chips is a novelty. And, in a world of 600-billion-parameter transformer-based LLMs, spiking neural networks (SNNs) are even more curious. In May, Innatera announced availability of Pulsar, a RISC-V chip that accelerates SNNs, a type of...

www.linkedin.com

 

[Slade]

Its better than the last one..

Ten times better

 

[TheDrooben]

Good 4c in my opinion

Have we finally reached the inflection point????.........revenue growing (albeit from a low base). Almost 12 months of cash left at this burn rate.....what will the next 4C bring?? Only 14 Notification regarding Unquoted Securities announcements until then.......

Happy as Larry

 

[7für7]

Bravo is nitpicking and TheDrooben would have loved to see the 35 million already today… Man, oh man, you guys are starting to sound like T&J and the dean… get it together… the process is gradual… both the growth and the transformation into bashers.

 

[Bravo]

This is a positive article for BrainChip. By listing neuromorphic computing as a core theme for “Future AI-as-a-Service,” NASSCOM implicitly endorses BrainChip’s innovation roadmap and relevance in India’s emerging tech landscape.

​

​

The Future of AIaaS: Quantum Computing, Neuromorphic Chips, and Next-Gen Architectures​

Shreesh Chaurasi
July 28, 2025



In 2019, Google's Sycamore quantum processor solved a calculation in 200 seconds that would take the world's fastest supercomputer 10,000 years. Fast forward to 2025, and we're witnessing the convergence of quantum computing, neuromorphic chips, and revolutionary architectures that promise to redefine AI-as-a-Service (AIaaS) from the ground up. The question isn't whether these technologies will transform enterprise AI—it's how quickly your organization can adapt to the seismic shift that's already underway.

The Current AIaaS Landscape: At the Precipice of Transformation​

Today's AIaaS market, valued at $15.7 billion in 2024 and projected to reach $148.4 billion by 2030, operates predominantly on traditional silicon-based architectures. However, we're approaching the physical limits of Moore's Law, with transistor scaling becoming increasingly challenging and expensive. Current GPU-based training of large language models like GPT-4 consumes approximately 50 GWh of electricity—enough to power 4,600 homes for a year. This computational bottleneck is driving the urgent need for revolutionary computing paradigms.
The enterprise reality is stark: 73% of organizations report that AI workloads are constrained by current computational limitations, while 68% cite energy costs as a significant barrier to AI adoption at scale. These challenges are catalyzing investment in next-generation computing architectures that promise to deliver exponential improvements in performance, efficiency, and capability.

Quantum Computing: Redefining the Computational Frontier​

The Quantum Advantage in AIaaS​

Quantum computing represents a fundamental departure from classical computing, leveraging quantum mechanical phenomena like superposition and entanglement to process information in ways that are impossible with traditional bits. For AIaaS providers, quantum computing offers unprecedented opportunities to tackle computationally intractable problems.
Current quantum systems like IBM's 1,121-qubit Condor processor and Google's 70-qubit Sycamore are already demonstrating quantum advantage in specific domains. By 2030, industry analysts predict quantum computers will achieve 1 million physical qubits, enabling fault-tolerant quantum computation that could revolutionize AI workloads.

Quantum Machine Learning: Beyond Classical Limitations​

Quantum machine learning (QML) algorithms are showing remarkable promise in several key areas:
Optimization Problems: Quantum annealing systems like D-Wave's Advantage can solve complex optimization problems with 5,000+ variables—critical for supply chain optimization, portfolio management, and resource allocation at enterprise scale.
Pattern Recognition: Quantum neural networks demonstrate exponential speedups for certain pattern recognition tasks, with recent research showing 16x improvements in training time for specific classification problems.
Cryptographic Applications: Quantum computers threaten current encryption methods but simultaneously enable quantum-secure communication protocols, creating new opportunities for secure AIaaS offerings.

Market Impact and Investment Trends​

The quantum computing market is experiencing explosive growth, with global investment reaching $2.4 billion in 2024. Major cloud providers are rapidly expanding quantum offerings:

• IBM Quantum Network: Over 200 members, including Fortune 500 companies
• Amazon Braket: Providing access to quantum hardware from multiple vendors
• Microsoft Azure Quantum: Full-stack quantum development platform
• Google Quantum AI: Focus on fault-tolerant quantum computing

Enterprise adoption is accelerating, with 32% of large organizations planning quantum computing investments within the next three years, primarily for optimization, simulation, and machine learning applications.

Neuromorphic Computing: Mimicking the Brain's Efficiency​

The Biological Inspiration​

The human brain processes information using approximately 20 watts of power—roughly equivalent to a light bulb—while performing complex cognitive tasks that require massive computational resources in traditional systems. Neuromorphic chips attempt to replicate this efficiency by mimicking the brain's neural structure and information processing methods.

Leading Neuromorphic Architectures​

Intel Loihi 2: This second-generation neuromorphic chip contains 1 million artificial neurons and supports up to 120 million synapses. In benchmark tests, Loihi 2 demonstrates 1000x better energy efficiency compared to conventional processors for certain AI workloads.
IBM TrueNorth: With 1 million programmable neurons and 256 million synapses, TrueNorth consumes only 65 milliwatts during operation—orders of magnitude less than traditional processors performing similar tasks.
BrainChip Akida: This commercial neuromorphic processor offers edge AI capabilities with ultra-low power consumption, targeting applications in autonomous vehicles, smart cameras, and IoT devices.

Neuromorphic Advantages for AIaaS​

Neuromorphic computing offers several compelling advantages for AIaaS providers:
Energy Efficiency: Neuromorphic chips can reduce power consumption by 100-1000x for specific AI workloads, dramatically reducing operational costs and environmental impact.
Real-time Processing: Event-driven processing enables microsecond response times, crucial for real-time AI applications like autonomous driving and industrial automation.
Adaptive Learning: Unlike traditional processors, neuromorphic chips can learn and adapt in real-time without explicit reprogramming, enabling more dynamic and responsive AI services.
Edge Computing: Ultra-low power consumption makes neuromorphic chips ideal for edge AI deployments, extending AIaaS capabilities to resource-constrained environments.

Next-Generation Architectures: Beyond Traditional Paradigms​

Photonic Computing: The Speed of Light​

Photonic computing leverages light instead of electricity to process information, offering potential advantages in speed, power efficiency, and parallel processing capabilities. Lightmatter's photonic interconnects have demonstrated 10x improvements in data center efficiency, while Xanadu's photonic quantum computers are exploring new frontiers in quantum machine learning.
Key benefits include:

• Bandwidth: Optical systems can handle terahertz frequencies, far exceeding electronic limitations
• Parallel Processing: Light-based systems can perform matrix operations in parallel, ideal for AI workloads
• Energy Efficiency: Photonic systems consume significantly less power than electronic equivalents

DNA Computing: Biological Information Processing​

DNA computing represents an exotic but promising approach to computation, leveraging the information storage and processing capabilities of biological molecules. Microsoft's DNA storage system has demonstrated the ability to store 1 exabyte of data in a space smaller than a sugar cube, with potential applications in long-term AI model storage and retrieval.

Hybrid Architectures: The Best of All Worlds​

The future of AIaaS likely lies not in any single technology but in hybrid architectures that combine the strengths of different computing paradigms:
Quantum-Classical Hybrids: Combining quantum processors for specific optimization tasks with classical systems for general computation Neuromorphic-Digital Integration: Using neuromorphic chips for real-time processing while leveraging traditional processors for training and complex calculations Photonic-Electronic Systems: Integrating photonic interconnects and processing units with electronic control systems

Industry Implications and Strategic Considerations​

Performance Revolution​

The convergence of these technologies promises dramatic performance improvements:
Training Efficiency: Quantum algorithms could reduce training time for certain neural networks from weeks to hours Inference Speed: Neuromorphic chips enable microsecond inference times for real-time applications Scalability: New architectures support massive parallelization, enabling AIaaS providers to serve millions of concurrent users

Cost Optimization​

Next-generation computing architectures offer significant cost advantages:
Energy Savings: Neuromorphic and photonic systems could reduce data center energy consumption by 90% Hardware Costs: Quantum cloud services eliminate the need for expensive quantum hardware investments Operational Efficiency: Automated optimization and adaptive systems reduce manual intervention requirements

New Service Categories​

These technologies enable entirely new categories of AIaaS offerings:
Quantum-Enhanced AI: Services that leverage quantum algorithms for optimization, simulation, and machine learning Ultra-Low Latency AI: Real-time AI services powered by neuromorphic chips Distributed Intelligence: Edge AI services that bring intelligence closer to data sources

Market Projections and Investment Opportunities​

Market Size and Growth​

The next-generation computing market is experiencing unprecedented growth:
Quantum Computing: $1.3 billion in 2024, projected to reach $15.4 billion by 2030 Neuromorphic Chips: $78 million in 2024, expected to grow to $7.8 billion by 2030 Photonic Computing: $1.8 billion in 2024, forecasted to reach $11.9 billion by 2030

Investment Landscape​

Venture capital and corporate investment in next-generation computing reached $8.2 billion in 2024, with major technology companies leading the charge:
Google: $3+ billion invested in quantum computing research and development IBM: $2.5 billion committed to quantum and neuromorphic computing Intel: $1.8 billion in neuromorphic and photonic computing initiatives Microsoft: $1.2 billion in quantum computing and related technologies

Implementation Roadmap for Enterprises​

Technical Challenges and Mitigation Strategies​

Quantum Computing Challenges​

Quantum Decoherence: Current quantum systems are extremely sensitive to environmental interference, limiting computation time to microseconds. Mitigation strategies include improved error correction codes, better isolation techniques, and hybrid quantum-classical algorithms that minimize quantum computation time.
Scalability: Building fault-tolerant quantum computers requires millions of physical qubits to create thousands of logical qubits. Companies should focus on near-term intermediate-scale quantum (NISQ) applications while hardware matures.
Skill Gap: Quantum programming requires specialized knowledge of quantum mechanics and novel programming paradigms. Organizations should invest in quantum education and partner with universities and specialized training providers.

Neuromorphic Computing Challenges​

Programming Complexity: Neuromorphic systems require fundamentally different programming approaches compared to traditional processors. Development of high-level programming frameworks and tools is essential for broader adoption.
Limited Ecosystems: The neuromorphic software ecosystem is still immature compared to traditional computing. Early adopters should focus on specific applications where neuromorphic advantages are clear and significant.
Integration Difficulties: Combining neuromorphic chips with existing infrastructure requires careful system design and potentially custom hardware solutions.

Security and Compliance Considerations​

Quantum Security Implications​

The advent of large-scale quantum computers poses both threats and opportunities for cybersecurity:
Cryptographic Vulnerabilities: Shor's algorithm could break current RSA and elliptic curve cryptography within decades. Organizations must begin transitioning to post-quantum cryptographic standards.
Quantum Key Distribution: Quantum communication protocols offer theoretically unbreakable security, creating new opportunities for secure AIaaS offerings.

Data Privacy in Neuromorphic Systems​

Neuromorphic systems' always-on, adaptive nature raises new privacy considerations:
Continuous Learning: Systems that continuously adapt based on input data require careful privacy controls and data governance frameworks.
Edge Processing: While edge processing reduces data transmission, it requires robust security measures to protect distributed processing nodes.

Environmental Impact and Sustainability​

Energy Efficiency Revolution​

Next-generation computing architectures offer dramatic improvements in energy efficiency:
Current State: Data centers consume approximately 1% of global electricity, with AI workloads representing a growing portion Future Potential: Neuromorphic and photonic systems could reduce AI computation energy requirements by 100-1000x

Carbon Footprint Reduction​

Organizations implementing next-generation architectures can achieve significant sustainability improvements:
Immediate Impact: Neuromorphic edge computing reduces data transmission requirements Long-term Benefits: Quantum algorithms could optimize supply chains and reduce waste across entire industries

Future Outlook: The Next Decade of AIaaS​

Technology Convergence Timeline​

2025-2026: Commercial deployment of NISQ algorithms for optimization problems 2027-2028: Widespread adoption of neuromorphic chips in edge applications 2029-2030: Fault-tolerant quantum computers enabling breakthrough AI capabilities

Competitive Landscape Evolution​

The Artificial Intelligence as a Service (AIaaS) market will likely consolidate around companies that successfully integrate next-generation computing architectures. Organizations that delay adoption risk being left behind as quantum advantage and neuromorphic efficiency create insurmountable competitive gaps.

Economic Impact​

McKinsey estimates that quantum computing could create $850 billion in annual value by 2040, with significant portions coming from quantum-enhanced AI applications. Similarly, neuromorphic computing could enable $120 billion in new edge AI applications by 2035.

Conclusion: Preparing for the Quantum-Neuromorphic Era​

The convergence of quantum computing, neuromorphic chips, and next-generation architectures represents the most significant transformation in computing since the advent of the microprocessor. For enterprise leaders, the message is clear: the organizations that begin preparing today will dominate the AIaaS landscape of tomorrow.
The path forward requires bold vision, strategic investment, and careful execution. Companies must balance the immediate benefits of current AI technologies with the transformative potential of emerging architectures. Those who successfully navigate this transition will not only achieve competitive advantages but will fundamentally reshape entire industries.
The quantum-neuromorphic era of AIaaS isn't just approaching—it's already begun. The question isn't whether these technologies will transform your business, but whether you'll be leading the transformation or struggling to catch up.

---

The Future of AIaaS: Quantum Computing, Neuromorphic Chips, and Next-Gen Architectures

In 2019, Google's Sycamore quantum processor solved a calculation in 200 seconds that would take the...

community.nasscom.in

 

[Baron Von Ricta]

Maybe we should turn the BRN thread into a dating site since so many of us are getting ex-communicated by our significant others.

I‘ll start the ball rolling.

I‘m a fun-loving lass with a quirky sense of humour and an even quirkier sense of fashion. I like to practice taekwondo in my spare time and to watch documentaries about true crime and evil psychopaths on Netflix, as well as eating tubs of Connoisseur ice-cream. I’m currently learning how to play the maracas and the bugle . I have 10 cats and a blue-tongued lizard called Gertrude.

I don’t like chewing sounds, so if you’re one of those people who can eat with your mouth closed, then please feel free to call me.

Bravo,

I can't even afford Netflix, so I guess I'm out!

 

[Yoda]

Where's my generational wealth? I WANT IT NOW!!!!

Maybe we should turn the BRN thread into a dating site since so many of us are getting ex-communicated by our significant others.

I‘ll start the ball rolling.

I‘m a fun-loving lass with a quirky sense of humour and an even quirkier sense of fashion. I like to practice taekwondo in my spare time and to watch documentaries about true crime and evil psychopaths on Netflix, as well as eating tubs of Connoisseur ice-cream. I’m currently learning how to play the maracas and the bugle . I have 10 cats and a blue-tongued lizard called Gertrude.

I don’t like chewing sounds, so if you’re one of those people who can eat with your mouth closed, then please feel free to call me.

OMG you're my Dream Woman!

 

[Bravo]

Bravo,

I can't even afford Netflix, so I guess I'm out!

Hi Baron,

That's OK.

Sometimes I watch the TV through my neighbours window when they're not looking, so I can save on the subscription fee.

 

[Diogenese]

I wonder if they’ll consider solving the strategic problem of bad grammar?

On a positive note, it's great to see some revenue, albeit a small amount. And nice to to hear about the "numerous high-quality customers and partners".


View attachment 89122

"Solve for" is just maths boffin speak

 

[Bravo]

"Solve for" is just maths boffin speak

Oh, I see, thanks for that clarification.

I'm not at all fluent in maths boffin speak. I'll edit my post accordingly.

 

[HopalongPetrovski]

Hi Baron,

That's OK.

Sometimes I watch the TV through my neighbours window when they're not looking, so I can save on the subscription fee.


View attachment 89126

Moi aussi. And isn't it great that the voices in your head are for free too?

 

[HopalongPetrovski]

Moi aussi. And isn't it great that the voices in your head are for free too?

Yes! Those one's.

 

[DingoBorat]

I'm personally happy with the quarterly.
It's still way too bland, spice it up a bit FFS!
Why 3 paragraphs about the redomiciling fiasco? The first was enough...

We actually have over US21 million in cash with 8.2 million from LDA coming in this quarter.
With the known balance of the 11.8 million LDA obligation before June 2026, there are no funding surprises going forward (other than the timing of the next capital call, which you would think will be next year?).

Have patents been granted and made without our knowledge?..

The 1.386 million dollars is "obviously" from the customer engagements listed?..


I'm very happy to see us in robotic dogs, I'll be happier to see us in Humanoid Robotics.

Unitree (Chinese) has recently released the R1 a HR for under 6000 USD! (only 1 hour run time, but impressive capabilities).
Competition for all other HR manufacturers, just reached another level...

 

[MrNick]

At least we weren't involved here... that really would ice a pretty tasteless cake.

Australian-made rocket crashes after attempted north Queensland launch

Gilmour Space Technologies attempted to launch its rocket from a spaceport in the north Queensland community of Bowen in an Australian-first on Wednesday morning.

www.abc.net.au

 

[Diogenese]

Maybe we should turn the BRN thread into a dating site since so many of us are getting ex-communicated by our significant others.

I‘ll start the ball rolling.

I‘m a fun-loving lass with a quirky sense of humour and an even quirkier sense of fashion. I like to practice taekwondo in my spare time and to watch documentaries about true crime and evil psychopaths on Netflix, as well as eating tubs of Connoisseur ice-cream. I’m currently learning how to play the maracas and the bugle . I have 10 cats and a blue-tongued lizard called Gertrude.

I don’t like chewing sounds, so if you’re one of those people who can eat with your mouth closed, then please feel free to call me.

Family-oriented motel owner with underwater car collection enjoys cos play water activities. Bring your own rocking chair. SWSWAF.

 

[H.E. Pennypacker]

Ladies and gentlemen, we have a Karen on board. Mimimimi


View attachment 89116

That “Karen” was an ex head at Mercedes..Francios opinion is pretty relevant.

 

[smoothsailing18]

Great to see $$ heading north , I wonder if these engineering fees are in full or progress payments ?....We have turned the corner finally, next quarter will be very interesting .

 

[Diogenese]

Great to see $$ heading north , I wonder if these engineering fees are in full or progress payments ?....We have turned the corner finally, next quarter will be very interesting .

The engineering fees indicate serious technical engagement with 3 customers.

I wonder when we can expect to see some of our share of the $1.8M US from the AFRL/RTX microDoppler radar SBIR.

https://www.highergov.com/contract-...l-processing-algorithm-af242-d015-sbir-e97d5/

Mapping Complex Sensor Signal Processing Algorithms onto Neuromorphic Chips​

ID: AF242-D015 • Type: SBIR / STTR Topic

...

OBJECTIVE: Develop an efficient workflow and approach for mapping complex RF and radar signal processing algorithms onto neuromorphic hardware.

The neuromorphic hardware can be a limited research prototype or a commercial product. The signal processing algorithms encompass processing of RF signals to decode communication waveforms, Multiple-Input Multiple-Output (MIMO) adaptive beamforming, Space-Time Adaptive Processing (STAP), Ground Moving Target Indicator radar, and generating Synthetic Aperture Radar (SAR) images from raw in-phase and quadrature data.

The goal is to outline a versatile approach that can translate algorithms as specified in the Matlab or Python software environment into a neuromorphic model implemented in physical hardware.

DESCRIPTION: The ubiquity of embedded RF devices and the Internet of Things (IoT) has motivated approaches to process data with less latency and power consumption [1]. Neuromorphic integrated circuit (IC) hardware has enabled new ultra-low power embedded RF and radar signal processing applications implemented through deep learning neural network (DLNN) models [2-4]. Neuromorphic hardware provides an advantage of a factor of 100 in power consumption per inference relative to emulation using a traditional Graphics Processing Unit (GPU) [5].

PHASE I: As this is a Direct-to-Phase-II (D2P2) topic, no Phase I awards will be made as a result of this topic. To qualify for this D2P2 topic, the Government expects the applicant(s) to demonstrate feasibility by means of a prior Phase I-type effort that does not constitute work undertaken as part of a prior or ongoing SBIR/STTR funding agreement. The required feasibility demonstration must include successfully developing advanced AI-based radio frequency (RF) algorithms and successfully porting them to a neuromorphic chip, with the final chip performing very well.

PHASE II: Using a HWIL approach, awardee(s) will measure the response of the neuromorphic hardware to RF and radar signals in real time. Awardee(s) will validate the performance of the neuromorphic hardware in terms of power consumption and timing latency. Awardee(s) will confirm that the outputs are deterministic and compare favorably to the expected values from the M&S environment.

PHASE III DUAL USE APPLICATIONS: The awardee(s) will identify potential commercial and dual use neuromorphic applications for the IoT such as MIMO adaptive beamforming.

REFERENCES: C. Xiao, J. Chen, and L. Wang, "Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI," Sensors, vol. 22, no. 19, p. 7248, 2022.
A. Baietto, J. Boubin, P. Farr, T. J. Bihl, A. M. Jones, and C. Stewart, "Lean neural networks for autonomous radar waveform design," Sensors, vol. 22, no. 4, p. 1317, 2022.
P. Farr, A. M. Jones, T. Bihl, J. Boubin, and A. DeMange, "Waveform design implemented on neuromorphic hardware," in 2020 IEEE International Radar Conference (RADAR), 2020, pp. 934-939: IEEE.
M. Barnell, C. Raymond, M. Wilson, D. Isereau, and C. Cicotta, "Target classification in synthetic aperture radar and optical imagery using loihi neuromorphic hardware," in 2020 IEEE High Performance Extreme Computing Conference (HPEC), 2020, pp. 1-6: IEEE.
C. D. Schuman, S. R. Kulkarni, M. Parsa, J. P. Mitchell, P. Date, and B. Kay, "Opportunities for neuromorphic computing algorithms and applications," Nature Computational Science, vol. 2, no. 1, pp. 10-19, 2022. (2023). RFView Family of Digital Engineering Tools. Available: https://www.islinc.com/products/rfview;
KEYWORDS: AI; Neuromorphic computing; Low C-SWAP; Embedded processing.

Phase Detail
Phase I: Establish the technical merit, feasibility, and commercial potential of the proposed R/R&D efforts and determine the quality of performance of the small business awardee organization.
Phase II: Continue the R/R&D efforts initiated in Phase I.

Funding is based on the results achieved in Phase I and the scientific and technical merit and commercial potential of the project proposed in Phase II. Typically, only Phase I awardees are eligible for a Phase II award
Duration: 6 Months - 1 Year

I'd expect an SBIR designed to encourage small business would have progress payments..

I'd also expect that, given the database for the model (RTX), Kris Carlson could do the job in his lunchtime.