Can Nvidia Survive the 4th Industrial Revolution?

Though Nvidia is riding high at the moment all indicators are that it has positioned itself on the wrong side of technology history.

While Nvidia has been compressing models to stave off the end of Moores Law its continued preoccupation with its Von Neumann market dominance has seen it embrace the false dawn offered to it by Large Language Models and the cult of ChatGPT.

The fragile nature of Nvidias technology future has been exposed in the last week by a small Australian technology company that has been stealthily developing an entirely new, some have said science fiction solution, to the energy resource issue exposed by the power and cost involved in training and running Large Language Models represented by ChatGPT.

The World has been fantasising about what is called Edge Computing for over a decade. The principle underpinning Edge Computing is actually very simple and can best be understood by what might be considered a strange example.

I am sure you have heard of terms like Food Miles, Buy Local, Eat Local, Grow Your Own as ways to decarbonise and save the planet. The simple indisputable proposition being if you reduce the distance between you and your food sources point of production the reduction in transport will reduce the energy consumed in putting the food on your plate.

Putting a bunch of flowers cut from your own garden on the sideboard is infinitely more fuel efficient than trucking, flying, trucking, driving fresh picked flowers from Europe around the globe to you in Australia.

Cutting asparagus in your kitchen garden is infinitely more efficient than buying asparagus from your Local Supermarket that has been cut in Peru and transported to you in Australia.

Now in the above examples I have chosen two products that require refrigeration to keep them fresh after picking to ensure they arrive at your home still useable and which as a result requires transport by jet airliners.

Suffice to say it is immediately obvious that if you are trying to reduce carbon and cost processing your flowers and asparagus at home wins hands down as zero carbon versus tonnes is a no brainer.

Now I know there are practical limitations making this solution difficult for those of us with black thumbs or living in home units to embrace. But that is an argument for another day.

The point is that this is what Edge Computing is all about. It is about reducing compute miles and in so doing cutting dramatically the cost of doing compute and carbon emissions.

For example take a Smart Doorbell. Currently a Smart Doorbell needs to be constantly connected via your homes wireless network to carry out its function of identifying and alerting you to the presence of someone at the door.

In lay terms 24/7 it sits there constantly processing camera frames showing the brick wall next to your front door and sending to the cloud over and over and over an image of the brick wall and receiving back message after message that no one is at the front door. Now you can reduce the power by slowing down the number of photos/frames it takes every second to monitor for someone coming to your front door but if the gap becomes too great between frames someone can come and go in that gap and avoid detection. So this method has built in limitations even when working to design but not so well when bandwidth is congested or connection breaks down.

Edge Computing is about as I said reducing the compute miles. By placing the compute close as possible to the Smart Doorbell if not right up against it you immediately reduce the distance between the camera/sensor and the compute/intelligence. This has the advantage of reducing power consumption, reducing latency (time it takes to send the message back and forth from the camera/sensor to the data centre) and preventing congesting the bandwidth with photos of a blank brick wall affecting your ability to stream Netflix or Sky Sport.

Everywhere Edge Computing is being spoken about and Nvidia as the dominant player in the computing space is calling out about its Edge Computing solutions.

There is probably not one person on the planet with any interest in computing who has not heard of Nvidia or its Nvidia Jetson range of edge computing solutions.

Indeed, the Nvidia Jeson range is a leader in this space across the globe. Its Jetson solutions are to be found everywhere but for how much longer can Jetson dominate for Nvidia when it is hamstrung by old thinking in a World that is transitioning towards the Fourth Industrial Revolution.

So lets take a quick look at what Nvidia publishes about the Nvidia Jetson AGX Orin series, the Jetson Orin NX series and the Jetson Orin Nano series by reference to the advertised performance figures.

Power ranges from 5 watts to 60 Watts

Power 15W 60W 15W 75W 15W 40W 10W 25W 10W 20W 7W 15W 7W 10W

TOPs ranges from 20 TOPs to 275 TOPs

AI Performance 275 TOPS 248 TOPS 200 TOPS 100 TOPS 70 TOPS 40 TOPS 20 TOPS

These numbers would certainly seem impressive to those who were feeding punch cards into the first IBM main frame computer even if you added on the power required to externally cool Jetson running some form of external cooling such as fans.

As impressive as these numbers are they clearly do not offer a power budget that can be reasonably embraced by those looking for an Edge Computing solution. Nvidia have to their credit recognised this and in consequence introduced the Jetson Nano TX2 Series and boasts that the Jetson TX2i, the Jetson TX2, the Jetson TX2 4GB and the Jetson TX2 NX come with AI Performance or 1.26 TFLOPS to 1.33 TFLOPS however the power budget ranges from 7.5 Watts to 20 Watts and on top of these numbers you need to allow for external cooling. You might have noticed that while Nvidia has reduced the form factor the power required remains in the multi watt region. (1)

To address these failings a new form of computing referred to as spiking neural network compute is being championed by Intel and IBM and they have over the last decade reached the point of proving out in research chips the huge benefits to be had by embracing this new style of compute not the least of which is a massively reduced power budget. The research at Intel and IBM goes on apace.

Enter stage left this little-known Australian company that has been listed on the Australian Stock Exchange since 2015. This tiny company with less than 100 employees has quietly some would say stealthily gone about its business and has beaten Intel and IBM to the punch launching its first commercial spiking neural network engineering chip in 2020 and is shortly to release its second generation technology, which it reports on its website as being capable of the following performance figures across three models or iterations of this technology advancement:


Max Efficiency

Ideal for always-on, energy-sipping Sensor Applications:

  • Vibration Detection
  • Anomaly Detection
  • Keyword Spotting
  • Sensor Fusion
  • Low-Res Presence Detection
  • Gesture Detection

Extremely Efficient @ Sensor Inference

Either Standalone or with Min-spec MCU.

Configurable to ideal fit:

  • 12 nodes (4 NPE/node)
  • Anomaly Detection
  • Keyword Spotting

Expected implementations:

  • 50 MHz 200
  • MHz Up to 100 GOPs

Additional Benefits

Eliminates need for CPU intervention

Fully accelerates most feed-forward networks

  • Optional skip connection and TENNs support for more complex networks
  • Completely customizable to fit very constrained power, thermal, and silicon area budgets
  • Enables energy-harvesting and multi-year battery life applications, sub milli-watt sensors


Sensor Balanced

Accelerates in hardware most Neural Network Functions:

  • Advanced Keyword Spotting
  • Sensor Fusion
  • Low-Res Presence Detection
  • Gesture Detection & Recognition
  • Object Classification
  • Biometric Recognition
  • Advanced Speech Recognition
  • Object Detection & Semantic Segmentation

Optimal for Sensor Fusion and Application SoCs

With Min-Spec or Mid-Spec MCU.

Configurable to ideal fit:

  • 38 nodes (4 NPE/node) 25 KB
  • 100 KB per NPE
  • Process, physical IP and other optimizations

Expected implementations:

Additional Benefits

  • CPU is free for most non-NN compute
  • CPU runs application with minimal NN-management
  • Completely customizable to fit very constrained power, thermal and silicon area budgets
  • Enables intelligent, learning-enabled MCUs and SoCs consuming tens to hundreds of milliwatts or less


Max Performance

Detection, Classification, Segmentation, Tracking, and ViT:

  • Gesture Detection
  • Object Classification
  • Advanced Speech Recognition
  • Object Detection & Semantic Segmentation
  • Advanced Sequence Prediction
  • Video Object Detection & Tracking
  • Vision Transformer Networks

Advanced Network-Edge Performance in a Sensor-Edge Power Envelope

With Mid-Spec MCU or Mid-Spec MPU.

Configurable to ideal fit:

  • 8256 nodes (4 NPE/node) + optional Vision Transformer
  • 100 KB per NPE
  • Process, physical IP and other optimizations

Expected implementations:

  • 800 MHz 2 GHz
  • Up to 131 TOPs

Additional Benefits

  • CPU is free for most non-NN compute

At this stage I cannot comment on the power budget of these iterations however we do know that the first released chip the AKD1000 which was able to retail for about $US25.00 had a power budget that ran in the micro to milliwatts and was claimed by Edge Impulse (5), Quantum Ventura (3 & 4) and Tata Consulting Services (1) to outperform a GPU from Nvidia by some considerable margin across all performance measurements and this new version is an advancement grown out of the underlying neural fabric supporting AKD1000.

Perhaps the most worrying recent doomsday prediction for Nvidia at the Edge came from Tata Elxsis Mr. Sunil Nair Vice President EMEA and Design Digital who posted on his LinkedIn page right beside a post about Tata Elxsi partnering with Nvidia in the Cloud the following:

Cloud computing is commodity. Edge is where the action is.

Thrilled to see Tata Elxsi and Brainchip partner to enable and integrate ultra-low power neuromorphic processors for use cases that would bring huge savings and transform citizen experience. (especially the ones over spending on Nvidia.)

Source: LinkedIn

Mr. Nair has been with Tata Elxsi since 1997. While the partnership with Tata Elxsi has only recently been announced Brainchip has been working with Tata Consulting Services (1) TATA Groups research arm since at least 2019 when they jointly presented AKD1000 performing a live gesture recognition demonstration. Since that time Tata Consulting Services has released a number of peer reviewed papers covering the use of AKD1000 and it can be said that Mr. Nair would be very well informed when it comes to the benefits that Brainchips AKIDA technology solutions can bring to the Edge.

The full release of the next generation referred to as AKIDA 2.0 up till today has been restricted to a number of select customers however the company has recently advised in a CEO investor presentation that the full public release is imminent. This prediction seems to be holding true as in the past week Brainchips website has been updated with substantial information regarding AKIDA 2.0 signalling it is getting close to the launch date.

The interesting aspect of Brainchip Inc is that while it has remained largely unknown to the general public and the investment world in its quiet way it has been accumulating a very long and impressive list of corporate and academic engagements including the following publicly acknowledged group and according to Mr. Rob Telson Vice President of Ecosystems & Partnerships, they have hundreds of companies testing AKIDA technology boards:

1. FORD, 2. VALEO, 3. RENESAS, 4. NASA, 5. TATA Consulting Services, 6. MEGACHIPS, 7. MOSCHIP, 8. SOCIONEXT, 9. PROPHESEE, 10. VVDN, 11. TEKSUN, 12. Ai LABS, 13. NVISO, 14. EMOTION 3D, 15. ARM, 16. EDGE IMPULSE, 17. INTEL Foundries, 18. GLOBAL FOUNDRIES, 19. BLUE RIDGE ENVISIONEERING, 20. MERCEDES BENZ, 21. ANT 61, 22. QUANTUM VENTURA, 23. INFORMATION SYSTEM LABORATORIES, 24. INTELLISENSE SYSTEMS, 25. CVEDIA, 26. LORSER INDUSTRIES, 27. SiFIVE, 28. IPRO Silicon IP, 29. SALESLINK, 30. NUMEN, 31. VORAGO, 32. NANOSE, 33. BIOTOME, 34. OCULI, 35. Magik Eye, 36. GMAC, 37. TATA Elxsi, 38. University of Oklahoma, 41. Arizona State University, 42. Carnegie Mellon University, 43. Rochester Institute of Technology, 44. Drexel University, 45. University of Virginia.

It should be noted that Brainchip has been at pains in its literature and presentations to explain that the AKIDA technology is processor and sensor agnostic and being fully digital, scalable, and portable across all foundries. The AKD1000 was produced successfully first time and every time in 28nm at TSMC and only recently the AKD1500 was received back from Global Foundries successfully fabricated in 22nm FDSOI first time and every time.

The individual who gave the underlying AKIDA technology life is Peter van der Made who is also one of the founders of Brainchip and his full vision is to create a beneficial form of Artificial General Intelligence by about 2030. This vision plays out in a series of steps and AKIDA 3.0 is presently in development and according to the companys CEO Sean Hehir in a very recent investor presentation each step is targeted to take 12 to 18 months. Historically Peter van der Made and his cofounder Anil Mankar have impressed with their capacity to deliver on their technology development time lines and by always having a little extra what they like to call secret sauce with each new technology reveal.

This little extra secret sauce with AKIDA 2.0 was the release of the TeNNs (6 & 9) and ViT (7) which provide an unprecedented leap into the future from what even the most optimistic expected to be possible at the far Edge using energy harvesting to power these devices. It is impossible to do justice to what they bring to the Edge Compute revolution in this article but fortunately even though patents are pending Brainchip has published a White Paper (6) and videos providing easy to follow plain English explanations. (7)

By the way anyone up for a bit of regression analysis (8) using AKIDA technology Brainchip also has that covered. When others were opining that spiking neural networks could not do regression analysis Brainchip Inc was demonstrating it running on AKD1000 for monitoring vibration in rail infrastructure.

There is so much to delight those who love to read about and explore science fiction becoming reality when peeling back the petals of the rose that is the Brainchip AKIDA technology revolution.

In concluding in Australia the ignorant and poor of intellect have treated at times the vision of Peter van der Made with a savagery of doubt usually reserved for those who claim to have been abducted by aliens and as is usually the case these critics have been members of the so called sophisticated investor class and even though they have little credibility in their areas of claimed expertise they drown in their own ignorance when it comes to the science of neuromorphic computing. If tempted to listen to such individuals about the science of neuromorphic computing one is well served to remember the life of Robert Goddard…1&cvid=297d1358fb8c45ea9b1ae445a4985d75&ei=51

My opinion only so DYOR

1.Low Power & Low Latency Cloud Cover Detection in Small Satellites Using On-board Neuromorphic Processors. Chetan Kadway, Sounak Dey, Arijit Mukherjee, Arpan Pal, Gilles Bzard 2023 International Joint Conference on Neural Networks (IJCNN), 18, 2023

Emergence of small satellites for earth observation missions has opened up new horizons for space research but at the same time posed newer challenges of limited power and compute resource arising out of the size & weight constraints imposed by these satellites. The currently evolving neuromorphic computing paradigm shows promise in terms of energy efficiency and may possibly be exploited here. In this paper, we try to prove the applicability of neuromorphic computing for on-board data processing in satellites by creating a 2-stage hierarchical cloud cover detection application for multi-spectral earth observation images. We design and train a CNN and convert it into SNN using the CNN2SNN conversion toolkit of Brainchip Akida neuromorphic platform. We achieve 95.46% accuracy while power consumption and latency are at least 35x and 3.4x more efficient respectively in stage-1 (and 230x & 7x in stage-2) compared to the equivalent CNN running on Jetson TX2.

2.An energy-efficient AkidaNet for morphologically similar weeds and crops recognition at the Edge. Vi Nguyen Thanh Le, Kevin Tsiknos, Kristofor D Carlson, Selam Ahderom. 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 18, 2022

Wild radish weeds have always been a persistent problem in agriculture due to their quick and undesirable spread. Therefore, the accurate identification and effective control of wild radish in canola crops at early growth stage play an indispensable role in reducing herbicide rates and enhancing agricultural productivity. In this paper, an energy efficient and lightweight AkidaNet model is developed to accurately identify broad-leaf weeds and crops with similar morphology at four different growth stages. Experiments performed on a published bccr-segset dataset show that our proposed method achieves competitive performance, a classification accuracy of 99.73%, compared to several well-known CNNs architectures. Next, we quantized and converted the model into a Spiking Neural Network for implementation on a spike-based neuromorphic hardware device. The converted model is not only superior in low-latency and low-power consumption but also retains a similar accuracy to the original model. We also employ Grad-CAM to validate whether our model focuses on important features in plant images to identify wild radish weeds in crops…r-weeds-and-crops-recognition-at-the-Edge.pdf

3.Table 6.…c65de04&pid=1-s2.0-S1877050922017860-main.pdf (Page 494)


In this federally funded phase 2 program, Quantum Ventura is creating state-of-the-art cybersecurity applications for the U.S. Department of Energy under the Small Business Innovation Research (SBIR) Program. The program is focused on Cyber threat-detection using neuromorphic computing, which aims to develop an advanced approach to detect and prevent cyberattacks on computer networks and critical infrastructure using brain-inspired artificial intelligence.

Neuromorphic computing is an ideal technology for threat detection because of its small size and power, accuracy, and in particular, its ability to learn and adapt, since attackers are constantly changing their tactics, said Srini Vasan, President and CEO of Quantum Ventura Inc. We believe that our solution incorporating Brainchips Akida will be a breakthrough for defending against cyber threats and address additional applications as well.

BrainChip and Quantum Ventura Partner to Develop Cyber Threat Detection


Running the out-of-the-box demos on the Akida Raspberry Pi development kit I have was very impressive achieving, according to the statistics, approximately 100 FPS for a 9-mW power dissipation.

All told I am very impressed with the BrainChip Akida neuromorphic processor. The performance of the networks implemented is very good, while the power used is also exceptionally low. These two parameters are critical parameters for embedded solutions deployed at the edge.

Project links

  1. Visal wake word:
  2. Anomaly detection:
  3. CIFAR10:
  4. Keyword spotting:

Adiuvo is a consultancy that provides embedded systems design, training, and marketing services. Taylor is Founder and Principal Consultant of the company, teaches about embedded systems at the University of Lincoln, and is host of the podcast The Embedded Hour.



8. BrainChip demonstrates Regression Analysis with Vibration Sensors

9. Temporal Event-Based Neural Networks: A new approach to Temporal Processing

Pg 8:
Unlike standard CNN networks that only operate on the spatial dimensions, TENNs contain both temporal and spatial convolution layers. They may combine spatial and temporal features of the data at all levels from shallow to deep layers. In addition, TENNs efficiently learn both spatial and temporal correlations from data in contrast with state-space models that mainly treat time series data with no spatial components. Given the hierarchical and causal nature of TENNs, relationships between elements that are both distant in space and time may be constructed for efficient continuous data processing (such as video, raw speech, and medical data).
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