To me it sounds like sparse matrix multiplication repackaged as "event-driven spiking computation", where the spikes are simply the non-zero elements that sparse GPU kernels have always been designed to process.
The supposedly dynamic/temporal nature of the model seems to be not applied for GPU execution, collapsing it into a single static computation equivalent to just applying a pre-calculated sparsity mask.
Perhaps a bit cynical of me, but it feels like wrapping standard sparse computing and operator fusion in complex, biological jargon...
The 'brain-inspired' community has always been doing this, since Carver Mead introduced the term 'neuromorphic' in the late 1980s. Reselling banalities as a new great insight. My favourite is "Neuromorphic computing breakthrough could enable blockchain on Mars" [1]. What else can they do? After all, that community has now multiple decades of failure under it's belt. Not a single success. Failure to make progress in AI and failure to say anything of interest about the brain. To paraphrase a US president: In this world nothing can be said to be certain, except death, taxes and neuromphicists exaggerating. (Aside: I was told by someone who applied to YC with a 'neuromorphic' startup that YC said, they don't fund 'neuromorphic'. I am not sure about details ...). The whole 'brain talk' malarkey goes back way longer.
In particular psychology and related subjects,
since their origins as a specialty in the 19th century, have heavily used brain-inspired metaphors that were intended to mislead. Already in the 19th century that was criticised. See [3] for an interesting discussion.
There is something interesting in this post, namely that it's based on non-Nvidia GPUs, in this case MetaX [2]. I don't know how competitive MetaX are today, but I would not bet against China in the longer term.
> I was told by someone who applied to YC with a 'neuromorphic' startup that YC said, they don't fund 'neuromorphic'.
There is something refreshingly consistent in a VC that is laser focused on enterprise CRM dashboard for dashboards workflow optimization ChatGPT wrappers that also filters out the neuromorphicists.
Reminds me of how the Samurai were so used to ritual dueling and reading their lineages before battle but when the Mongolians encountered them they just shot the samurai mid-speech.
This is 100% true. It's just very effective and profitable PR. There IS a minority stream in the field that uses the branding to get funding and then builds real tech. But you'll never know it as neuromorphic as the label comes off once it works. Look up Synaptics (touch pad) history.
I believe the argument is that you can also encode information in the time domain.
If we just look at spikes as a different numerical representation, then they are clearly inferior. For example, consider that encoding the number 7 will require seven consecutive pulses on a single spiking line. Encoding the number in binary will require one pulse on three parallel lines.
Binary encoding wins 7x in speed and 7/3=2.333x in power efficiency...
On the other hand, if we assume that we are able to encode information in the gaps between pulses, then things quickly change.
I think the main benefit of a neuromorphic design would be to make it dataflow driven (asynchronous event driven - don't update neuron outputs unless their inputs change) rather than synchronous, which is the big power efficiency unlock. This doesn't need to imply a spiking design though - that seems more of an implementation detail, at least as far as dataflow goes. Nature seems to use spike firing rates to encode activation strength.
In the brain the relative timing/ordering of different neurons asynchronously activating (A before B, or B before A) is also used (spike-timing-dependent plasticity - STDP) as a learning signal to strengthen or weaken connection strengths, presumably to learn sequence prediction in this asynchronous environment.
STDP also doesn't imply that spikes or single neuron spike train inter-spike timings are necessary - an activation event with a strength and timestamp would
seem to be enough to implement a digital dataflow design, although ultimately a custom analog design may be more efficient.
Can you explain the benefit of renaming dataflow as 'neuromorphic'?
You do understand that dataflow architectures have been tried many many times? See [1] for a brief history. MIT had a bit dataflow lab for many years (lead by the recently deceased Arvind).
What is the benefit of re-inventing dataflow architectures by complete amateurs who are not at all aware of the 1/2 century research tradition on dataflow architecture, and the very clear and concrete reasons when this architecture has so far failed whenever it was tried for general purpose processors?
We can not even apply Santayana's "those who forget their history are condemned to repeat it because the 'neuromorphic' milieu doesn't even bother understanding this history.
> Can you explain the benefit of renaming dataflow as 'neuromorphic'?
Neuromorphic just means brain-like or brain inspired, and brains operate in asynchronous dataflow type fashion.
I'm not sure how you read into what I wrote that I was "renaming dataflow as neuromorphic", which was certainly not what I meant.
I wonder if you regard Steve Furber (who I knew from Acorn), designer of the ARM CPU, as a "complete amateur"? He also designed the AMULET async processors, as well as for that matter the SpiNNaker system for spiking neural network research.
In any case, async (dataflow) processor design, while complex, clearly isn't an impossible task, and at some point in the future when the need arises (mobile robotics?) and there is sufficient financial incentive, I expect we'll see it utilized in commercial systems.
I'm not sure why you focus on "general purpose processors" given that we're talking about ANNs and neuromorphic systems. A custom chip would seem a better bet if the goal is to minimize power usage.
I don't rate Furber as a "complete amateur", but he's the exception in this milieu.
> Neuromorphic just means brain-like or brain inspired,
I don't even see any evidence that 'neuromorphic' architecture is brain inspired in a non-trivial sense. Can you please provide evidence, for example, a non-trivial mapping between 'neuromorphic' architectures (say SpiNNaker) and the SOTA models of the brain that we have, e.g. the existing data-driven model simulating C. elegans brain (the MetaWorm project)?
As Steve Furber also says (personal communication): we don't know enough of how the brain works to have computer architectures that can meaningfully inspired by brains. The "neuro-" prefix is marketing. [1] documents this use and dates it back to the 19th century. See also
• Neuroergonomics
• Neurotypical
• Neurodivergent
• Neurodiverse
• Neurosis
• Neuroethics
• Neuroeconomics
• Neuromarketing
• Neurolaw
• Neurosecurity
• Neuropsychology
• Neuropsychoanalysis
• Neurotheology
• Neuro-Linguistic Programming
• Neurogastronomy
I have seen all the above used without irony.
> brains operate in asynchronous dataflow type fashion.
That's a questionable statement. To the best of my knowledge, there is no consensus as of 2025 of how to model even a single neuron. (Function of synapse is even less understood).
When I asked Steve Furber what 'neuromorphic meant, he said: "There are many things today described as neuromphric. Mead would not call SpiNNaker as neuromorphic!"
Furber also said: "Neuromorphic status: attracts no money, but works (in the sense of accelerate in niche domains)". Upon my asking what niche domains, he said: "brain simulation but nothing else". (He was referring to SpiNNacker). I asked him if SpiNNaker can accelerate back-propagation and he said: "no, because the brain does not do back-propagation".
> async (dataflow) processor design, while complex, clearly isn't an impossible task
I did not say it was impossible. It has been done many times, see my references to Arvind's lab at MIT (I spent some time there). The problem with async (dataflow) processor design is that it consistently fails to live up to its promises (PPA). There are specific technical reason for that that are quite well understood.
> why you focus on "general purpose processors" given that we're talking about ANNs and neuromorphic systems.
Because the 'neuromorphic' marketing often reads like they want to build more efficient 'brain-inspired' general purpose computers. Certainly the dataflow architectures (a la Arvind/MIT) tried to. This is one of the many issues with the 'neuromorphic' milieu: they are really vague about their goals. If they would restrict their claims to certain classes of accelerators, then their claims would be less delusional.
> the goal is to minimize power usage.
If that is the goal, they are also not very successful. On CMOS silicon changes from 0 to 1 or 0 to 1 is what consumes most of the power, this would make the constant spiking expensive, no?
> I don't even see any evidence that 'neuromorphic' architecture is brain inspired in a non-trivial sense
I'm just giving the definition of the word neuromorphic. Individual systems claiming to be neuromorphic can be judged on their own merits, but that does not change the definition of the word.
>> brains operate in asynchronous dataflow type fashion.
>That's a questionable statement. To the best of my knowledge, there is no consensus as of 2025 of how to model even a single neuron
You're confusing two things - do we know absolutely everything there is to know about every type of neuron and synapse, in order to build a 100% accurate model of them? No. Do we know that neurons operate asynchronously to each other, only activating when their inputs change? Yes.
> When I asked Steve Furber what 'neuromorphic meant, he said: "There are many things today described as neuromphric. Mead would not call SpiNNaker as neuromorphic!"
Of course not - it was just an ARM-based message passing system, built to be able to efficiently simulate spiking neural networks (modelled as message passing), but with no particular specialization for that task.
> I asked him if SpiNNaker can accelerate back-propagation and he said: "no, because the brain does not do back-propagation".
That's a silly question, and you got the silly answer you deserved. SpiNNaker is basically just an ARM cluster with fast message passing. It can be used to accelerate anything relative to running on slower hardware. If you wanted to train an ANN on it, using backprop, you certainly could, but why would you WANT to mix spiking neural nets and backprop ?!
> If that is the goal, they are also not very successful. On CMOS silicon changes from 0 to 1 or 0 to 1 is what consumes most of the power, this would make the constant spiking expensive, no?
Sure, but biological neurons don't spike constantly. They spike only when an input changes that causes them to activate - maybe on average only 10's of times per second. This is the whole point of an async (no clock)/dataflow chip design - for chip elements to only consume power when their inputs change - not to route a GHz clock to them that continuously draws power (by flipping between 0 and 1 billions of times a second) even if the element's inputs are only changing 10's of times a second (or whatever).
"I believe the argument is that you can also encode information in the time domain."
Brain research showed that's happening, too. You'll see many models like this if you DuckDuckGo for "spiking" "temporal" "encoding" or subtitute "time" for temporal. You can further use "neural" "network" or "brain" focus it on sub-fields.
They do look pretty good compared to the two other linear (non-Transformer) models. Conventional attention is hard to beat in benchmarks but it is quadratic in time and memory complexity.
> Our architectural choices are closely aligned with principles observed in biological brains.
How? They point out three design choices: linear attention, MoE layers, and spike coding.
Apparently linear attention is brain-inspired because it can be viewed as a "simplified abstraction of dendritic dynamics with multi-branch morphology." Who knows what that means exactly [1]. They don't discuss it further. MoE layers apparently reflect "a principle of modular specialization." Fine, whatever.
Now, using a dozen attention variants + MoE is bog standard. The real novelty would be spike coding. Page 11 is dedicated to the different ways they could turn signals into spike trains, including such biologically-inspired mechanisms as using two's complement. However, they don't actually do spike coding in a time domain. In their implementation, "spike coding" apparently means to turn activations into integers. Section 3.3.3 claims that this lets us simulate an underlying spiking neural network, so we can validate the spiking approach without using special hardware. But if your SNN can be simulated faithfully on a GPU by turning things into integers, isn't that a bit of a depressing SNN?
Either I'm missing something, or this is just just dressing standard techniques with loads of meaningless jargon. Of course that’s a very popular way to operate in deep learning nowadays.
[1] Like, attention can draw from multiple tokens, sort of like how different spines of a dendrite can draw from multiple axons? Can’t make this stuff up.
>The current implementation adopts pseudo-spiking, where activations are approximated as spike-like signals at the tensor level, rather than true asynchronous event-driven spiking on neuromorphic hardware.
Isn't that in essence very similar to Quantization Aware Training (QaT)?
Can you explain more? Why would that be the case? What is being passed from one layer to the next is not a linear value but the delay until the next spike, which is very different.
But I understand that they simulate the spikes as integer events in the forward pass (as described here https://github.com/BICLab/Int2Spike) and calculate a continuous gradient based on high resolution weights for the backward pass.
This seems to be very similar to the straight-through-estimator (STE) approach that us usually used for quantization aware training. I may be wrong though.
SpikingBrain treats 'spikes' as 1-bit quantization stickers. True neural-level sparsity should be input-dependent, time-resolved, and self-organized during learning. If a new circuit diagram cannot 'grow' with every forward pass, then don't blame everyone for treating it as Another Sparse Marketing - oh wait, Neuromorphic Marketing.
To me it sounds like sparse matrix multiplication repackaged as "event-driven spiking computation", where the spikes are simply the non-zero elements that sparse GPU kernels have always been designed to process.
The supposedly dynamic/temporal nature of the model seems to be not applied for GPU execution, collapsing it into a single static computation equivalent to just applying a pre-calculated sparsity mask.
Perhaps a bit cynical of me, but it feels like wrapping standard sparse computing and operator fusion in complex, biological jargon...
The 'brain-inspired' community has always been doing this, since Carver Mead introduced the term 'neuromorphic' in the late 1980s. Reselling banalities as a new great insight. My favourite is "Neuromorphic computing breakthrough could enable blockchain on Mars" [1]. What else can they do? After all, that community has now multiple decades of failure under it's belt. Not a single success. Failure to make progress in AI and failure to say anything of interest about the brain. To paraphrase a US president: In this world nothing can be said to be certain, except death, taxes and neuromphicists exaggerating. (Aside: I was told by someone who applied to YC with a 'neuromorphic' startup that YC said, they don't fund 'neuromorphic'. I am not sure about details ...). The whole 'brain talk' malarkey goes back way longer. In particular psychology and related subjects, since their origins as a specialty in the 19th century, have heavily used brain-inspired metaphors that were intended to mislead. Already in the 19th century that was criticised. See [3] for an interesting discussion.
There is something interesting in this post, namely that it's based on non-Nvidia GPUs, in this case MetaX [2]. I don't know how competitive MetaX are today, but I would not bet against China in the longer term.
[1] https://cointelegraph.com/news/neuromorphic-computing-breakt...
[2] https://en.wikipedia.org/wiki/MetaX
[3] K. S. Kendler, A history of metaphorical brain talk in psychiatry. https://www.nature.com/articles/s41380-025-03053-6
> I was told by someone who applied to YC with a 'neuromorphic' startup that YC said, they don't fund 'neuromorphic'.
There is something refreshingly consistent in a VC that is laser focused on enterprise CRM dashboard for dashboards workflow optimization ChatGPT wrappers that also filters out the neuromorphicists.
Reminds me of how the Samurai were so used to ritual dueling and reading their lineages before battle but when the Mongolians encountered them they just shot the samurai mid-speech.
There is — some of these things make money and the others don’t :)
[dead]
This is 100% true. It's just very effective and profitable PR. There IS a minority stream in the field that uses the branding to get funding and then builds real tech. But you'll never know it as neuromorphic as the label comes off once it works. Look up Synaptics (touch pad) history.
I believe the argument is that you can also encode information in the time domain.
If we just look at spikes as a different numerical representation, then they are clearly inferior. For example, consider that encoding the number 7 will require seven consecutive pulses on a single spiking line. Encoding the number in binary will require one pulse on three parallel lines.
Binary encoding wins 7x in speed and 7/3=2.333x in power efficiency...
On the other hand, if we assume that we are able to encode information in the gaps between pulses, then things quickly change.
I think the main benefit of a neuromorphic design would be to make it dataflow driven (asynchronous event driven - don't update neuron outputs unless their inputs change) rather than synchronous, which is the big power efficiency unlock. This doesn't need to imply a spiking design though - that seems more of an implementation detail, at least as far as dataflow goes. Nature seems to use spike firing rates to encode activation strength.
In the brain the relative timing/ordering of different neurons asynchronously activating (A before B, or B before A) is also used (spike-timing-dependent plasticity - STDP) as a learning signal to strengthen or weaken connection strengths, presumably to learn sequence prediction in this asynchronous environment.
STDP also doesn't imply that spikes or single neuron spike train inter-spike timings are necessary - an activation event with a strength and timestamp would seem to be enough to implement a digital dataflow design, although ultimately a custom analog design may be more efficient.
Can you explain the benefit of renaming dataflow as 'neuromorphic'?
You do understand that dataflow architectures have been tried many many times? See [1] for a brief history. MIT had a bit dataflow lab for many years (lead by the recently deceased Arvind). What is the benefit of re-inventing dataflow architectures by complete amateurs who are not at all aware of the 1/2 century research tradition on dataflow architecture, and the very clear and concrete reasons when this architecture has so far failed whenever it was tried for general purpose processors?
We can not even apply Santayana's "those who forget their history are condemned to repeat it because the 'neuromorphic' milieu doesn't even bother understanding this history.
[1] https://csg.csail.mit.edu/Dataflow/talks/DennisTalk.pdf
> Can you explain the benefit of renaming dataflow as 'neuromorphic'?
Neuromorphic just means brain-like or brain inspired, and brains operate in asynchronous dataflow type fashion.
I'm not sure how you read into what I wrote that I was "renaming dataflow as neuromorphic", which was certainly not what I meant.
I wonder if you regard Steve Furber (who I knew from Acorn), designer of the ARM CPU, as a "complete amateur"? He also designed the AMULET async processors, as well as for that matter the SpiNNaker system for spiking neural network research.
In any case, async (dataflow) processor design, while complex, clearly isn't an impossible task, and at some point in the future when the need arises (mobile robotics?) and there is sufficient financial incentive, I expect we'll see it utilized in commercial systems.
I'm not sure why you focus on "general purpose processors" given that we're talking about ANNs and neuromorphic systems. A custom chip would seem a better bet if the goal is to minimize power usage.
I don't rate Furber as a "complete amateur", but he's the exception in this milieu.
> Neuromorphic just means brain-like or brain inspired,
I don't even see any evidence that 'neuromorphic' architecture is brain inspired in a non-trivial sense. Can you please provide evidence, for example, a non-trivial mapping between 'neuromorphic' architectures (say SpiNNaker) and the SOTA models of the brain that we have, e.g. the existing data-driven model simulating C. elegans brain (the MetaWorm project)?
As Steve Furber also says (personal communication): we don't know enough of how the brain works to have computer architectures that can meaningfully inspired by brains. The "neuro-" prefix is marketing. [1] documents this use and dates it back to the 19th century. See also
• Neuroergonomics
• Neurotypical
• Neurodivergent
• Neurodiverse
• Neurosis
• Neuroethics
• Neuroeconomics
• Neuromarketing
• Neurolaw
• Neurosecurity
• Neuropsychology
• Neuropsychoanalysis
• Neurotheology
• Neuro-Linguistic Programming
• Neurogastronomy
I have seen all the above used without irony.
> brains operate in asynchronous dataflow type fashion.
That's a questionable statement. To the best of my knowledge, there is no consensus as of 2025 of how to model even a single neuron. (Function of synapse is even less understood).
When I asked Steve Furber what 'neuromorphic meant, he said: "There are many things today described as neuromphric. Mead would not call SpiNNaker as neuromorphic!"
Furber also said: "Neuromorphic status: attracts no money, but works (in the sense of accelerate in niche domains)". Upon my asking what niche domains, he said: "brain simulation but nothing else". (He was referring to SpiNNacker). I asked him if SpiNNaker can accelerate back-propagation and he said: "no, because the brain does not do back-propagation".
> async (dataflow) processor design, while complex, clearly isn't an impossible task
I did not say it was impossible. It has been done many times, see my references to Arvind's lab at MIT (I spent some time there). The problem with async (dataflow) processor design is that it consistently fails to live up to its promises (PPA). There are specific technical reason for that that are quite well understood.
> why you focus on "general purpose processors" given that we're talking about ANNs and neuromorphic systems.
Because the 'neuromorphic' marketing often reads like they want to build more efficient 'brain-inspired' general purpose computers. Certainly the dataflow architectures (a la Arvind/MIT) tried to. This is one of the many issues with the 'neuromorphic' milieu: they are really vague about their goals. If they would restrict their claims to certain classes of accelerators, then their claims would be less delusional.
> the goal is to minimize power usage.
If that is the goal, they are also not very successful. On CMOS silicon changes from 0 to 1 or 0 to 1 is what consumes most of the power, this would make the constant spiking expensive, no?
[1] K. S. Kendler, A history of metaphorical brain talk in psychiatry. https://www.nature.com/articles/s41380-025-03053-6
> I don't even see any evidence that 'neuromorphic' architecture is brain inspired in a non-trivial sense
I'm just giving the definition of the word neuromorphic. Individual systems claiming to be neuromorphic can be judged on their own merits, but that does not change the definition of the word.
>> brains operate in asynchronous dataflow type fashion. >That's a questionable statement. To the best of my knowledge, there is no consensus as of 2025 of how to model even a single neuron
You're confusing two things - do we know absolutely everything there is to know about every type of neuron and synapse, in order to build a 100% accurate model of them? No. Do we know that neurons operate asynchronously to each other, only activating when their inputs change? Yes.
> When I asked Steve Furber what 'neuromorphic meant, he said: "There are many things today described as neuromphric. Mead would not call SpiNNaker as neuromorphic!"
Of course not - it was just an ARM-based message passing system, built to be able to efficiently simulate spiking neural networks (modelled as message passing), but with no particular specialization for that task.
> I asked him if SpiNNaker can accelerate back-propagation and he said: "no, because the brain does not do back-propagation".
That's a silly question, and you got the silly answer you deserved. SpiNNaker is basically just an ARM cluster with fast message passing. It can be used to accelerate anything relative to running on slower hardware. If you wanted to train an ANN on it, using backprop, you certainly could, but why would you WANT to mix spiking neural nets and backprop ?!
> If that is the goal, they are also not very successful. On CMOS silicon changes from 0 to 1 or 0 to 1 is what consumes most of the power, this would make the constant spiking expensive, no?
Sure, but biological neurons don't spike constantly. They spike only when an input changes that causes them to activate - maybe on average only 10's of times per second. This is the whole point of an async (no clock)/dataflow chip design - for chip elements to only consume power when their inputs change - not to route a GHz clock to them that continuously draws power (by flipping between 0 and 1 billions of times a second) even if the element's inputs are only changing 10's of times a second (or whatever).
https://en.wikipedia.org/wiki/Frequency-division_multiplexin...
The brain is doing shit like this.
And more, I suspect.
> you can also encode information in the time domain.
Also known as a serial interface. They are very successful: PCIe lane, SATA, USB.
These interfaces use serialized binary encoding.
SNNs are more similar to pulse density modulation (PDM), if you are looking for an electronic equivalent.
"I believe the argument is that you can also encode information in the time domain."
Brain research showed that's happening, too. You'll see many models like this if you DuckDuckGo for "spiking" "temporal" "encoding" or subtitute "time" for temporal. You can further use "neural" "network" or "brain" focus it on sub-fields.
Never underestimate the power of marketing.
https://github.com/BICLab/SpikingBrain-7B/blob/main/assets/t...
Shouldn’t one bold the better numbers?
Inspired by GPT-5 presentation :)
Did they ever address that? I have not been able to stop thinking about it, it was so bizarre.
Well, then none of their model's numbers would be bold and that's not what they/AIs usually see in publications!
They do look pretty good compared to the two other linear (non-Transformer) models. Conventional attention is hard to beat in benchmarks but it is quadratic in time and memory complexity.
The technical report says (page 7):
> Our architectural choices are closely aligned with principles observed in biological brains.
How? They point out three design choices: linear attention, MoE layers, and spike coding.
Apparently linear attention is brain-inspired because it can be viewed as a "simplified abstraction of dendritic dynamics with multi-branch morphology." Who knows what that means exactly [1]. They don't discuss it further. MoE layers apparently reflect "a principle of modular specialization." Fine, whatever.
Now, using a dozen attention variants + MoE is bog standard. The real novelty would be spike coding. Page 11 is dedicated to the different ways they could turn signals into spike trains, including such biologically-inspired mechanisms as using two's complement. However, they don't actually do spike coding in a time domain. In their implementation, "spike coding" apparently means to turn activations into integers. Section 3.3.3 claims that this lets us simulate an underlying spiking neural network, so we can validate the spiking approach without using special hardware. But if your SNN can be simulated faithfully on a GPU by turning things into integers, isn't that a bit of a depressing SNN?
Either I'm missing something, or this is just just dressing standard techniques with loads of meaningless jargon. Of course that’s a very popular way to operate in deep learning nowadays.
[1] Like, attention can draw from multiple tokens, sort of like how different spines of a dendrite can draw from multiple axons? Can’t make this stuff up.
[dead]
SpikingBrain Technical Report: Spiking Brain-inspired Large Models https://arxiv.org/abs/2509.05276
https://news.ycombinator.com/item?id=45206420
Well, it would still allow to deploy the trained model to SNN hardware, if it existed.
So significantly worse than qwen2.5, kinda useless in the current landscape. but always fun with more arcitechtures.
>The current implementation adopts pseudo-spiking, where activations are approximated as spike-like signals at the tensor level, rather than true asynchronous event-driven spiking on neuromorphic hardware.
Isn't that in essence very similar to Quantization Aware Training (QaT)?
Can you explain more? Why would that be the case? What is being passed from one layer to the next is not a linear value but the delay until the next spike, which is very different.
It was also a question from my side. :)
But I understand that they simulate the spikes as integer events in the forward pass (as described here https://github.com/BICLab/Int2Spike) and calculate a continuous gradient based on high resolution weights for the backward pass.
This seems to be very similar to the straight-through-estimator (STE) approach that us usually used for quantization aware training. I may be wrong though.
They compare to Llama3.1 which is 13 months old and qwen 2.5 which is 9 months old. And they don’t beat qwen.
They use MetaX GPUs instead NVDIA's...? This point is actually more surprising.
SpikingBrain treats 'spikes' as 1-bit quantization stickers. True neural-level sparsity should be input-dependent, time-resolved, and self-organized during learning. If a new circuit diagram cannot 'grow' with every forward pass, then don't blame everyone for treating it as Another Sparse Marketing - oh wait, Neuromorphic Marketing.
In a few years China will be completely independent from Nvidia.
https://en.wikipedia.org/wiki/MetaX
They have GPU manufacturers that nobody in the west has ever heard of.
They need TSMC for that.
Then they'll have no reason to conquer Taiwan.
/s
it's funny to observe how picky and cynical the HN crowd suddenly becomes when the disruptive technology is from china
What part of this is disruptive? It kind of has to work well to be disruptive, doesn't it?
You can't be critical anymore?
deepseek is from china and all their papers have been very well received