BRN Discussion Ongoing

[smoothsailing18]

Thinking out aloud, one at least of these engineering fee's recently announced would IMO be related to the PICO design. Sean did mention they we're asked for this specific design so brn made this for them, so then this would be for an IOT device is my guess.
I see them purchasing millions of these designs in 2026.

 

[Stable Genius]

Yesterday, the 𝗖𝗔𝗥𝗟 𝗽𝗿𝗼𝗷𝗲𝗰𝘁, 𝗖𝗼𝗹𝗹𝗶𝘀𝗶𝗼𝗻 𝗔𝘃𝗼𝗶𝗱𝗮𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, was officially launched with a successful kickoff meeting. | Nurjana Technologies

Yesterday, the 𝗖𝗔𝗥𝗟 𝗽𝗿𝗼𝗷𝗲𝗰𝘁, 𝗖𝗼𝗹𝗹𝗶𝘀𝗶𝗼𝗻 𝗔𝘃𝗼𝗶𝗱𝗮𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, was officially launched with a successful kickoff meeting. The project is developed by Nurjana Technologies and Politecnico di Milano for the European Space Agency - ESA. 𝗖𝗔𝗥𝗟 is designed to provide an innovative...

www.linkedin.com

Sorry if already posted, I noticed these guys were No. 69 on FF's list. Any chance we could be involved?

@Rach2512 there’s a chance as they’ve been liking and commenting on Brainchip for 1-2 yrs. An employee recently commented she’d been trialling Akida for 12 months to which a TATA employee black-catted her to tell her TATA’s used, trialled etc BC since 2019 (that was the software version from faded memory of a robot on a screen copying actions from a camera).

The company provides goods and services in space which is right up our alley. Also a Staalion project (may not be spelt right) we might be helping them with.

Sorry for the vagueness of the reply but I run off my phone and memory and often I get confused with who’s doing what with who as there is so many players in the ecosystem. I reckon the ESA is also involved with the Staalion project.

Until we see the licence, contract and revenue though it’s one of many in the pipeline.

 

[manny100]

Innatera is 100% pure competition.
There is no "hope" of them using any BrainChip IP.

They just found another way to "skin the cat" is all.

According to my reading they are best fit for different market segments.
I cannot find any peer reviewed papers comparing them.
Innatera does not use the words 'on chip learning' as Brainchip does but talks about real time 'real time intelligence' and 'adaption'. According to CEO Kumar see EET times article "the main limitation of the Innatera fabric is that it is not self learning, Kumar said noting that the neuron types are fixed, chosen for their suitability for a wide range of pattern recognition. While functions cannot be changed, parameters can be, he said."

https://www.eetimes.com/innatera-adds-more-accelerators-to-spiking-microcontroller/?hl=en-AU

Interesting the different methods used.
by both.
It would be great to see a peer review comparison.
Until then I am a bit uncertain as to the extent of competition Innatra offer.
The EET times article is a good read

 

[Diogenese]

According to my reading they are best fit for different market segments.
I cannot find any peer reviewed papers comparing them.
Innatera does not use the words 'on chip learning' as Brainchip does but talks about real time 'real time intelligence' and 'adaption'. According to CEO Kumar see EET times article "the main limitation of the Innatera fabric is that it is not self learning, Kumar said noting that the neuron types are fixed, chosen for their suitability for a wide range of pattern recognition. While functions cannot be changed, parameters can be, he said."

https://www.eetimes.com/innatera-adds-more-accelerators-to-spiking-microcontroller/?hl=en-AU

Interesting the different methods used.
by both.
It would be great to see a peer review comparison.
Until then I am a bit uncertain as to the extent of competition Innatra offer.
The EET times article is a good read

Hi manny,

AN Innantara patent application tries to capture all means of converting analog to spike train, but leans heavily on VCO )voltage controlled oscillator) in the description.

WO2024023111A1 SYSTEM AND METHOD FOR EFFICIENT FEATURE-CENTRIC ANALOG TO SPIKE ENCODERS 20220725

A signal processing circuit for a spiking neural network, comprising an interface for converting an analog input signal to a corresponding spike-time representation of the analog input signal. The interface comprises an analog-to-information (A/information) converter configured to produce a modulated signal which represents one or more features of the analog input signal; a feature detector circuit configured to compare the modulated signal with a reference signal representing a reference feature, and configured to produce an error signal indicating a difference between the modulated signal and the reference signal; a feature extractor circuit, which comprises a locked loop circuit having an input for receiving the error signal and configured to produce an output signal representing an occurrence of one or more of the features represented by the modulated signal; and an encoder circuit, which is configured to encode the output signal into spike trains for input to the spiking neural network.

[0092] … The A/frequency converter 32B may comprise a voltage- controlled oscillator (VCO).

4. The signal processing circuit of any of the preceding claims, wherein the feature is one or more of
i) specific characteristics, such as transient features, steady-state features,
ii) specific properties, such as (non)linearity features, statistical features, stationary features, transferfunction features, energy-content and/or based on iii) specific domain features, such as time-, delay-, frequency-, phase-domain features,
preferably wherein the A/information converter comprises an analog-to-time converter which converts the analog input signal into a modulated signal which represents certain timedomain features such as delay, frequency and/or phase..

[00101] The type of encoding used in encoding circuit 35 may vary depending on the type of parameters used in the converter 32, detector 33 and feature extractor 34. When looking at the delay parameter, one could use time-to-first spike (TTFS), inter-spike interval (ISI), burst, or delay synchrony encoding. When looking at the frequency parameter, rate or frequency synchrony encoding might be used. When looking at the phase parameter, phase or phase synchrony encoding might be used.

"Time-to-first-spike" sounds a bit like Rank-order coding which we obtained from Spikenet which uses the order of arrival, not specifically time of arrival.

This one is for ML/federated learning, not on-chip learning:

WO2025012331A1 METHOD FOR TRAINING MACHINE LEARNING MODELS FOR STOCHASTIC SUBSTRATES 20230711

The present invention relates to a method for training signal processing pipeline for deployment to a programmable fabric of a target device. The method comprises obtaining a model and characterization data of the components of the target device, obtaining programmable parameter values of the signal processing pipeline. Next, a plurality of target devices is simulated. The simulated target devices are based on the characterization data, such that the simulated target devices represent digital twins and/or the stochastic variability of the plurality of target devices. Optimization methods are used to compute updates of the programmable parameter values of the programmable parameters for each of the simulated target devices independently, after which the programmable parameter value updates are reduced to a single update of the programmable parameter values of the signal processing pipeline.

[0070] After a system performance threshold is passed or convergence is reached, a complete description of the principal network can be deployed to any number of target hardware devices in step 109, making up the hardware deployment 100C.

 

[DingoBorat]

According to my reading they are best fit for different market segments.
I cannot find any peer reviewed papers comparing them.
Innatera does not use the words 'on chip learning' as Brainchip does but talks about real time 'real time intelligence' and 'adaption'. According to CEO Kumar see EET times article "the main limitation of the Innatera fabric is that it is not self learning, Kumar said noting that the neuron types are fixed, chosen for their suitability for a wide range of pattern recognition. While functions cannot be changed, parameters can be, he said."

https://www.eetimes.com/innatera-adds-more-accelerators-to-spiking-microcontroller/?hl=en-AU

Interesting the different methods used.
by both.
It would be great to see a peer review comparison.
Until then I am a bit uncertain as to the extent of competition Innatra offer.
The EET times article is a good read

I'm not doubting that our technology is superior Manny, but when it comes to the low end applications that Pulsar is aimed at, that doesn't really matter.

They are going for the "low hanging fruit" something BrainChip has never really been focused on.
We've always been looking at the Big End of Town.

We now have the AKIDA E and Pico (although AKIDA 1.0 IP was always available with a minimum of "Nodes" Renesas only licenced 2 or something?)

But that requires more investment and commitment from an OEM (and more time) to design in "tape out" a chip etc, than Innatera is offering, with an OTSC.

Actual performance comparisons, or some extra features such as on chip learning (which probably aren't as necessary for low end applications) don't really mean much at that end, when you are looking at the investment and commitment differences for the OEMs.

I'm heavily invested here and am on BrainChip's and your "side" I'm just being impartial and honest about this.

We don't have any idea of how much commercial progress Innatera is having with Pulsar.
They may be facing as many or more market penetration issues and acceptance than us.
And if that's the case, it may end up being a big mistake for them to be mass producing their chips.

 

[Baneino]

According to my reading they are best fit for different market segments.
I cannot find any peer reviewed papers comparing them.
Innatera does not use the words 'on chip learning' as Brainchip does but talks about real time 'real time intelligence' and 'adaption'. According to CEO Kumar see EET times article "the main limitation of the Innatera fabric is that it is not self learning, Kumar said noting that the neuron types are fixed, chosen for their suitability for a wide range of pattern recognition. While functions cannot be changed, parameters can be, he said."

https://www.eetimes.com/innatera-adds-more-accelerators-to-spiking-microcontroller/?hl=en-AU

Interesting the different methods used.
by both.
It would be great to see a peer review comparison.
Until then I am a bit uncertain as to the extent of competition Innatra offer.
The EET times article is a good read

The two companies hardly compete directly because they are targeting different areas of application. While BrainChip aims to deliver the "universal AI brain" for edge computing, Innatera focuses on ultra-specialized sensor solutions with minimal energy consumption.
There is no clear "winner". I believe both can be successful if they play to their strengths:
BrainChip via broad partner networks and more universal architecture.
Innatera through radical energy efficiency and sensor integration in niche markets