BRN Discussion Ongoing

[Deleted member 118]

C’mon Rocket, you know to never say never when it comes to our wonderful BrainChip

I know but with games being played and nothing great from the Nasdaq overnight, I can see us being in the green by about 7% today

 

[Slade]

$1.50 by noon

 

[Cgc516]

Yesterday was 1.42

 

[misslou]

Could someone who knows stuff please have a look at this and confirm ours is better?

https://www.eetimes.com/neuromorphic-chip-gets-1-million-in-pre-orders/

 

[VictorG]

Could someone who knows stuff please have a look at this and confirm ours is better?

https://www.eetimes.com/neuromorphic-chip-gets-1-million-in-pre-orders/

I suppose to reduce their power usage they cut back on some neurons.
I'm not a tech geek but I don't see any similarities to AKIDA.

 

[buena suerte :-)]

Could someone who knows stuff please have a look at this and confirm ours is better?

https://www.eetimes.com/neuromorphic-chip-gets-1-million-in-pre-orders/

Hey misslou ... Dodgy Knees will no doubt shed some light on this!! .... GREEN today please

 

[uiux]

Could someone who knows stuff please have a look at this and confirm ours is better?

https://www.eetimes.com/neuromorphic-chip-gets-1-million-in-pre-orders/

Misslou

It's interesting that the Grai VIP is "a third–gen product" - so we are essentially comparing Akida v1 to Graimatter v3

It's on 12nm, which means they can squeeze a lot more into it

From cursory glance, Akida has 1.2 million neurons and 10 billion synapses vs VIP ones 18 million neurons... Grai only list "parameters" though and don't specify synapses

My understanding is the # of parameters that the chip can hold is calculated by doing something with the # of neurons and the # of synapses - I'll look for how this is calculated, but I remember @FrederikSchack digging in extremely deeply into this line of questioning, so maybe he could shed some light on how neurons/synapses/parameters are calculated

BrainChip has identified Grai Matter as the "nearest competitor" in their AGM presentation

There is a bunch of shared history between the two companies:

All roads lead to JAST

What is JAST? https://www.researchgate.net/profile/Amirreza_Yousefzadeh/publication/319410728_Unsupervised_learning_of_repeating_patterns_using_a_novel_STDP_based_algorithm/links/5b4932cc0f7e9b4637d5e6ba/Unsupervised-learning-of-repeating-patterns-using-a-novel-STDP-based-algorithm.pdf...

thestockexchange.com.au

Hopefully this is helpful

 

[jtardif999]

Misslou

It's interesting that the Grai VIP is "a third–gen product" - so we are essentially comparing Akida v1 to Graimatter v3

It's on 12nm, which means they can squeeze a lot more into it

From cursory glance, Akida has 1.2 million neurons and 10 billion synapses vs VIP ones 48 million neurons... Grai only list "parameters" though and don't specify synapses

My understanding is the # of parameters that the chip can hold is calculated by doing something with the # of neurons and the # of synapses - I'll look for how this is calculated, but I remember @FrederikSchack digging in extremely deeply into this line of questioning, so maybe he could shed some light on how neurons/synapses/parameters are calculated

BrainChip has identified Grai Matter as the "nearest competitor" in their AGM presentation

There is a bunch of shared history between the two companies:

All roads lead to JAST

What is JAST? https://www.researchgate.net/profile/Amirreza_Yousefzadeh/publication/319410728_Unsupervised_learning_of_repeating_patterns_using_a_novel_STDP_based_algorithm/links/5b4932cc0f7e9b4637d5e6ba/Unsupervised-learning-of-repeating-patterns-using-a-novel-STDP-based-algorithm.pdf...

thestockexchange.com.au

Hopefully this is helpful

Synapses are collocated memory containing the synaptic weights associated with the memory of learning things. So if their Neuromorphic architecture doesn’t contain Synapses how would anything be learned or indeed training be stored? Their Neuromorphic architecture is not real Kung Fu

 

[uiux]

Synapses are collocated memory containing the synaptic weights associated with the memory of learning things. So if their Neuromorphic architecture doesn’t contain Synapses how would anything be learned or indeed training be stored? Their Neuromorphic architecture is not real Kung Fu

If it's got neurons you would assume synapses follow

My understanding is that there's a relationship between neurons and synapses, like a sliding scale. You can decrease the number of neurons for more synapses and vice versa.

Maybe Graimatter see more value in advertising # parameters vs # of synapses




I see zero mention of learning rules associated with the device. It can't perform on-chip learning or one-shot learning..

 

[uiux]

Could someone who knows stuff please have a look at this and confirm ours is better?

https://www.eetimes.com/neuromorphic-chip-gets-1-million-in-pre-orders/

Misslou, as per the headline:

$1 million in pre–orders

Versus

Megachips $2 million + royalties

 

[equanimous]

The following paragraph from the GrAi article when compared with the Nviso BRN demonstration is telling as to 1,000 fps (also somewhere I saw the number 1670 fps but cannot recall where might have been said by Tim Llewellyn):

“GrAI VIP can handle MobileNetv1–SSD running at 30fps for 184 mW, around 20× the inferences per second per Watt compared to a comparable GPU, the company said, adding that further optimizations in sparsity and voltage scaling could improve this further”

They are saying they are comparable with GPU performance per watt while AKIDA according to Nviso is 10 times better than the Jetson Nano 100 fps.

AKD1000 too cool for school. It does this at $US10 to $US15 a chip on 28nm and as you scale down to 12nm its performance will increase so GrAi are much more expensive at 12nm and well behind.

The article makes clear they do not have one shot and incremental learning.

They are using 16 bit activations and boast only a 1 percent accuracy lose when converting from 32 bit activations. AKD1000 at 4 bit activations is offering only 1 percent loss because it has on chip convolution as well as the SNN.

 

[Iseki]

Could someone who knows stuff please have a look at this and confirm ours is better?

https://www.eetimes.com/neuromorphic-chip-gets-1-million-in-pre-orders/

From this article, it's a whole SoC, not IP that can be applied to any new small chips coming out.
It requires dual Arm M7 ( extremely high powered CPU's) to operate it
and it uses floating point maths (ie Graphics processing which involves power and heat.)

So not competition, except possibly for a small number of specialised applications. Certainly not at the edge.

 

[uiux]

The following paragraph from the GrAi article when compared with the Nviso BRN demonstration is telling as to 1,000 fps (also somewhere I saw the number 1670 fps but cannot recall where might have been said by Tim Llewellyn):

“GrAI VIP can handle MobileNetv1–SSD running at 30fps for 184 mW, around 20× the inferences per second per Watt compared to a comparable GPU, the company said, adding that further optimizations in sparsity and voltage scaling could improve this further”

They are saying they are comparable with GPU performance per watt while AKIDA according to Nviso is 10 times better than the Jetson Nano 100 fps.

AKD1000 too cool for school. It does this at $US10 to $US15 a chip on 28nm and as you scale down to 12nm its performance will increase so GrAi are much more expensive at 12nm and well behind.

The article makes clear they do not have one shot and incremental learning.

They are using 16 bit activations and boast only a 1 percent accuracy lose when converting from 32 bit activations. AKD1000 at 4 bit activations is offering only 1 percent loss because it has on chip convolution as well as the SNN.

It's not a fair comparison looking at NVISOs reported FPS and Grais - they are running two different neural network architectures

On Akida hardware, Nviso were only running the gaze tracking in that graph which from memory was running on 5 nodes compared to mobilenets ~30-123 - which is a significant footprint increase

Model zoo performances — Akida Examples documentation

doc.brainchipinc.com

 

[Slade]

Think we have to stop thinking of Akida as a chip and rather think of it as agnostic IP that improves the performance and efficiency of all chips. There will always be a place for Akida.

 

[uiux]

From this article, it's a whole SoC, not IP that can be applied to any new small chips coming out.
It requires dual Arm M7 ( extremely high powered CPU's) to operate it
and it uses floating point maths (ie Graphics processing which involves power and heat.)

So not competition, except possibly for a small number of specialised applications. Certainly not at the edge.

The article says they are aiming for near sensor applications. Though the wording of the article makes appear a little cagey, eg.

"GrAI Matter sees its offering in between edge server chips and tinyML, though its device is intended to sit next to sensors in the system. An ideal use case would be GrAI VIP next to a camera in a compact camera module, he added."

 

[Deleted member 118]

The shorter is giving it a good go this morning and there is only 1 thing I need to say.

 

[uiux]

It's not a fair comparison looking at NVISOs reported FPS and Grais - they are running two different neural network architectures

On Akida hardware, Nviso were only running the gaze tracking in that graph which from memory was running on 5 nodes compared to mobilenets ~30-123 - which is a significant footprint increase

Model zoo performances — Akida Examples documentation

doc.brainchipinc.com

Actually, nviso did port all their models over.... So not entirely sure about # of required nodes of all models..... Either way, it's not a fair comparison with Mobilenet as apples vs oranges

 

[Deleted member 118]

Accumulator is back and picking everything up at $1.13

 

[Bravo]

United Kingdom AI in Transportation Market Research Report 2022: Focus on Computer Vision, Context Awareness, Deep Learning, Natural Language Processing - Forecast to 2027 - ResearchAndMarkets.com

The "United Kingdom AI in Transportation Market, By Machine Learning Technology (Computer Vision, Context Awareness, Deep Learning, Natural Language Processi...

www.businesswire.com

 

[skutza]

Here is what the shorters drink after work on a Friday.