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We really need new hardware optimized for sparse compute. Deep Learning models would work way better with much higher dimensional sparse vectors but current har

by m_ke 11mo ago

We really need new hardware optimized for sparse compute. Deep Learning models would work way better with much higher dimensional sparse vectors but current hardware only excels at dense GMMs and structured sparsity.

carterschonwald 11mo ago
There also needs to be tools that can author that code! Im starting to dust off some ideas I developed over a decade ago to build such a toolkit. Recently realized “egads, my stuff can express almost every major gpu / cpu optimization that’s relevant for modern deep learning… need to do a new version with an eye towards adoption in that area”. Plus every flavor of sparse. Also need to figure out if some of the open core ideas i have in mind would be attractive to early stage investors who focus on the so-called deep tech end of the space. Definitely looks like ill have to do ye olde ask friends and acquaintances if they can point me to those folks approach since cold reach out historically is full of fail
p1esk 11mo ago
Deep Learning models would work way better with much higher dimensional sparse vectors Citations?
- yvdriess 11mo ago
  There has been plenty of evidence over the year. I don't have my bibliography handy right now, but you can find them looking for sparse training or lottery ticket hypothesis papers. The intuition is that ANNs make better predictions on high dimensional data, sparse weights can train the sparsity pattern as you train the weights, that the effective part of dense models are actually sparse (CFR pruning/sparsification research), and that dense models grow too much in compute complexity to further increase model dimension sizes.
  - p1esk 11mo ago
    I could not find any evidence that sparse models work better than dense models.
    - m_ke 11mo ago
      https://transformer-circuits.pub/2022/toy_model/index.html https://transformer-circuits.pub/2022/toy_model/index.html https://arxiv.org/abs/1803.03635 https://arxiv.org/abs/1803.03635 EDIT: don't have time to write it up, but here's gemini 3 with a short explanation: To simulate the brain's efficiency using Transformer-like architectures, we would need to fundamentally alter three layers of the stack: the *mathematical representation* (moving to high dimensions), the *computational model* (moving to sparsity), and the *physical hardware* (moving to neuromorphic chips). Here is how we could simulate a "Brain-Like Transformer" by combining High-Dimensional Computing (HDC) with Spiking Neural Networks (SNNs). ### 1\. The Representation: Hyperdimensional Computing (HDC) Current Transformers use "dense" embeddings—e.g., a vector of 4,096 floating-point numbers (like `[0.1, -0.5, 0.03, ...]`). Every number matters. To mimic the brain, we would switch to *Hyperdimensional Vectors* (e.g., 10,000+ dimensions), but make them *binary and sparse*. * **Holographic Representation:** In HDC, concepts (like "cat") are stored as massive randomized vectors of 1s and 0s. Information is distributed "holographically" across the entire vector. You can cut the vector in half, and it still retains the information (just noisier), similar to how brain lesions don't always destroy specific memories. * **Math without Multiplication:** In this high-dimensional binary space, you don't need expensive floating-point matrix multiplication. You can use simple bitwise operations: * **Binding (Association):** XOR operations (`A ⊕ B`). * **Bundling (Superposition):** Majority rule (voting). * **Permutation:** Bit shifting. * **Simulation Benefit:** This allows a Transformer to manipulate massive "context windows" using extremely cheap binary logic gates instead of energy-hungry floating-point multipliers. ### 2\. The Architecture: "Spiking" Attention Mechanisms Standard Attention is $O(N^2)$ because it forces every token to query every other token. A "Spiking Transformer" simulates the brain's "event-driven" nature. * **Dynamic Sparsity:** Instead of a dense matrix multiplication, neurons would only "fire" (send a signal) if their activation crosses a threshold. If a token's relevance score is low, it sends *zero* spikes. The hardware performs *no* work for that connection. * **The "Winner-Take-All" Circuit:** In the brain, inhibitory neurons suppress weak signals so only the strongest "win." A simulated Sparse Transformer would replace the Softmax function (which technically keeps all values non-zero) with a **k-Winner-Take-All** function. * *Result:* The attention matrix becomes 99% empty (sparse). The system only processes the top 1% of relevant connections, similar to how you ignore the feeling of your socks until you think about them. ### 3\. The Hardware: Neuromorphic Substrate Even if you write sparse code, a standard GPU (NVIDIA H100) is bad at running it. GPUs like dense, predictable blocks of numbers. To simulate the brain efficiently, we need *Neuromorphic Hardware* (like Intel Loihi or IBM NorthPole). * **Address Event Representation (AER):** Instead of a "clock" ticking every nanosecond forcing all neurons to update, the hardware is asynchronous. It sits idle (consuming nanowatts) until a "spike" packet arrives at a specific address. * **Processing-in-Memory (PIM):** To handle the high dimensionality (e.g., 100,000-dimensional vectors), the hardware moves the logic gates *inside* the RAM arrays. This eliminates the energy cost of moving those massive vectors back and forth. ### Summary: The Hypothetical "Spiking HD-Transformer" | Feature | Standard Transformer | Simulated "Brain-Like" Transformer | | :--- | :--- | :--- | | *Dimension* | Low (\~4k), Dense, Float32 | *Ultra-High* (\~100k), Sparse, Binary | | *Operation* | Matrix Multiplication (MACs) | *Bitwise XOR / Popcount* | | *Attention* | Global Softmax ($N^2$) | *Spiking k-Winner-Take-All* (Linear) | | *Activation* | Continuous (RELU/GELU) | *Discrete Spikes* (Fire-or-Silence) | | *Hardware* | GPU (Synchronous) | *Neuromorphic* (Asynchronous) |
  - noosphr 11mo ago
    If you can give that bibliography I'd love to read it. I have the same intuition and a few papers seem to support it but more and explicit ones would be much better.
yvdriess 11mo ago
Yes! I'de been advocating for it inside the industry for a decade, but it is an uphill battle. The researchers can't easily publish that kind of work (even Google researchers) because you don't have the hardware that can realistically train decently large models. The hardware companies don't want to take the risk a rethinking the architecture CPU or accelerator for sparse compute because there are no large existing customers.
leogao 11mo ago
For what it's worth, we think it's unfortunately quite unlikely that frontier models will ever be trained with extreme unstructured sparsity, even with custom sparsity optimized hardware. Our main hope is that understanding sub-frontier models can still help a lot with ensuring safety of frontier models; an interpretable GPT-3 would be a very valuable object to have. It may also be possible to adapt our method to only explaining very small but important subsets of the model.
- esafak 11mo ago
  As the lead author, why do you think so?
  - leogao 11mo ago
    I'm not an expert at hardware, so take this with a grain of salt, but there are two main reasons: - Discrete optimisation is always going to be harder than continuous optimization. Learning the right sparsity mask is fundamentally a very discrete operation. So even just matching fully continuous dense models in optimization efficiency is likely to be difficult. Though perhaps we can get some hope from the fact that MoE is also similarly fundamentally discrete, and it works in practice (we can think of MoE as incurring some penalty from imperfect gating, which is more than offset by the systems benefits of not having to run all the experts on every forward pass). Also, the optimization problem gets harder when the backwards pass needs to be entirely sparsified computation (see appendix B). - Dense matmuls are just fundamentally nicer to implement in hardware. Systolic arrays have nice predictable data flows that are very local. Sparse matmuls with the same number of flops nominally only need (up to a multiplicative factor) the same memory bandwidth as an equivalent dense matmul, but they need to be able to route data from any memory unit to any vector compute unit - the locality of dense matmuls means that the computation of each tile only requires a small slice of both input matrices, so we only need to load those slices into shared memory; on the other hand, because GPU-to-GPU transfers are way slower, when we op-shard matmuls, we replicate the data that is needed. Sparse matmuls would need either more replication within each compute die, or more all-to-all internal bandwidth. This means spending way more die space on huge crossbars and routing. This would cost a lot of die space, though thankfully, the crossbars consume much less power than actual compute, so perhaps this could match dense in energy efficiency and not make thermals worse. It also seems very likely that once we create the interpretable GPT-1 (or 2, or 3) we will find that making everything unstructured sparse was overkill, and there are much more efficient pretraining constraints we can apply to models to 80/20 the interpretability. In general, a lot of my hope routes through learning things like this from the intermediate artifact (interpretable GPT-n). To be clear, it doesn't seem literally impossible that with great effort, we could create custom hardware, and vastly improve the optimization algorithms, etc, such that weight-sparse models could be vaguely close in performance to weight-dense models. It's plausible that with better optimization the win from arbitrary connectivity patterns might offset the hardware difficulties, and I could be overlooking something that would make the cost less than I expect. But this would require immense effort and investment to merely match current models, so it seems quite unrealistic compared to learning something from interpretable GPT-3 that helps us understand GPT-5.
kwillets 11mo ago
My last dive into matrix computations was years ago, but the need was the same back then. We could sparsify matrices pretty easily, but the infrastructure was lacking. Some things never change.
- yvdriess 11mo ago
  On the software side I can recommend https://github.com/DrTimothyAldenDavis/GraphBLAS https://github.com/DrTimothyAldenDavis/GraphBLAS It is hard to make a sparse linear algebra framework, but Tim Davis has been doing a great job collecting the various optimal algorithms I to a single framework that acts more like an algebra than a collection of kernels.

by m_ke 11mo ago

carterschonwald 11mo ago

There also needs to be tools that can author that code! Im starting to dust off some ideas I developed over a decade ago to build such a toolkit. Recently realized “egads, my stuff can express almost every major gpu / cpu optimization that’s relevant for modern deep learning… need to do a new version with an eye towards adoption in that area”. Plus every flavor of sparse. Also need to figure out if some of the open core ideas i have in mind would be attractive to early stage investors who focus on the so-called deep tech end of the space. Definitely looks like ill have to do ye olde ask friends and acquaintances if they can point me to those folks approach since cold reach out historically is full of fail

p1esk 11mo ago

Deep Learning models would work way better with much higher dimensional sparse vectors Citations?

yvdriess 11mo ago
There has been plenty of evidence over the year. I don't have my bibliography handy right now, but you can find them looking for sparse training or lottery ticket hypothesis papers. The intuition is that ANNs make better predictions on high dimensional data, sparse weights can train the sparsity pattern as you train the weights, that the effective part of dense models are actually sparse (CFR pruning/sparsification research), and that dense models grow too much in compute complexity to further increase model dimension sizes.
- p1esk 11mo ago
  I could not find any evidence that sparse models work better than dense models.
  - m_ke 11mo ago
    https://transformer-circuits.pub/2022/toy_model/index.html https://transformer-circuits.pub/2022/toy_model/index.html https://arxiv.org/abs/1803.03635 https://arxiv.org/abs/1803.03635 EDIT: don't have time to write it up, but here's gemini 3 with a short explanation: To simulate the brain's efficiency using Transformer-like architectures, we would need to fundamentally alter three layers of the stack: the *mathematical representation* (moving to high dimensions), the *computational model* (moving to sparsity), and the *physical hardware* (moving to neuromorphic chips). Here is how we could simulate a "Brain-Like Transformer" by combining High-Dimensional Computing (HDC) with Spiking Neural Networks (SNNs). ### 1\. The Representation: Hyperdimensional Computing (HDC) Current Transformers use "dense" embeddings—e.g., a vector of 4,096 floating-point numbers (like `[0.1, -0.5, 0.03, ...]`). Every number matters. To mimic the brain, we would switch to *Hyperdimensional Vectors* (e.g., 10,000+ dimensions), but make them *binary and sparse*. * **Holographic Representation:** In HDC, concepts (like "cat") are stored as massive randomized vectors of 1s and 0s. Information is distributed "holographically" across the entire vector. You can cut the vector in half, and it still retains the information (just noisier), similar to how brain lesions don't always destroy specific memories. * **Math without Multiplication:** In this high-dimensional binary space, you don't need expensive floating-point matrix multiplication. You can use simple bitwise operations: * **Binding (Association):** XOR operations (`A ⊕ B`). * **Bundling (Superposition):** Majority rule (voting). * **Permutation:** Bit shifting. * **Simulation Benefit:** This allows a Transformer to manipulate massive "context windows" using extremely cheap binary logic gates instead of energy-hungry floating-point multipliers. ### 2\. The Architecture: "Spiking" Attention Mechanisms Standard Attention is $O(N^2)$ because it forces every token to query every other token. A "Spiking Transformer" simulates the brain's "event-driven" nature. * **Dynamic Sparsity:** Instead of a dense matrix multiplication, neurons would only "fire" (send a signal) if their activation crosses a threshold. If a token's relevance score is low, it sends *zero* spikes. The hardware performs *no* work for that connection. * **The "Winner-Take-All" Circuit:** In the brain, inhibitory neurons suppress weak signals so only the strongest "win." A simulated Sparse Transformer would replace the Softmax function (which technically keeps all values non-zero) with a **k-Winner-Take-All** function. * *Result:* The attention matrix becomes 99% empty (sparse). The system only processes the top 1% of relevant connections, similar to how you ignore the feeling of your socks until you think about them. ### 3\. The Hardware: Neuromorphic Substrate Even if you write sparse code, a standard GPU (NVIDIA H100) is bad at running it. GPUs like dense, predictable blocks of numbers. To simulate the brain efficiently, we need *Neuromorphic Hardware* (like Intel Loihi or IBM NorthPole). * **Address Event Representation (AER):** Instead of a "clock" ticking every nanosecond forcing all neurons to update, the hardware is asynchronous. It sits idle (consuming nanowatts) until a "spike" packet arrives at a specific address. * **Processing-in-Memory (PIM):** To handle the high dimensionality (e.g., 100,000-dimensional vectors), the hardware moves the logic gates *inside* the RAM arrays. This eliminates the energy cost of moving those massive vectors back and forth. ### Summary: The Hypothetical "Spiking HD-Transformer" | Feature | Standard Transformer | Simulated "Brain-Like" Transformer | | :--- | :--- | :--- | | *Dimension* | Low (\~4k), Dense, Float32 | *Ultra-High* (\~100k), Sparse, Binary | | *Operation* | Matrix Multiplication (MACs) | *Bitwise XOR / Popcount* | | *Attention* | Global Softmax ($N^2$) | *Spiking k-Winner-Take-All* (Linear) | | *Activation* | Continuous (RELU/GELU) | *Discrete Spikes* (Fire-or-Silence) | | *Hardware* | GPU (Synchronous) | *Neuromorphic* (Asynchronous) |
- noosphr 11mo ago
  If you can give that bibliography I'd love to read it. I have the same intuition and a few papers seem to support it but more and explicit ones would be much better.

yvdriess 11mo ago

Yes! I'de been advocating for it inside the industry for a decade, but it is an uphill battle. The researchers can't easily publish that kind of work (even Google researchers) because you don't have the hardware that can realistically train decently large models. The hardware companies don't want to take the risk a rethinking the architecture CPU or accelerator for sparse compute because there are no large existing customers.

leogao 11mo ago

For what it's worth, we think it's unfortunately quite unlikely that frontier models will ever be trained with extreme unstructured sparsity, even with custom sparsity optimized hardware. Our main hope is that understanding sub-frontier models can still help a lot with ensuring safety of frontier models; an interpretable GPT-3 would be a very valuable object to have. It may also be possible to adapt our method to only explaining very small but important subsets of the model.

esafak 11mo ago
As the lead author, why do you think so?
- leogao 11mo ago
  I'm not an expert at hardware, so take this with a grain of salt, but there are two main reasons: - Discrete optimisation is always going to be harder than continuous optimization. Learning the right sparsity mask is fundamentally a very discrete operation. So even just matching fully continuous dense models in optimization efficiency is likely to be difficult. Though perhaps we can get some hope from the fact that MoE is also similarly fundamentally discrete, and it works in practice (we can think of MoE as incurring some penalty from imperfect gating, which is more than offset by the systems benefits of not having to run all the experts on every forward pass). Also, the optimization problem gets harder when the backwards pass needs to be entirely sparsified computation (see appendix B). - Dense matmuls are just fundamentally nicer to implement in hardware. Systolic arrays have nice predictable data flows that are very local. Sparse matmuls with the same number of flops nominally only need (up to a multiplicative factor) the same memory bandwidth as an equivalent dense matmul, but they need to be able to route data from any memory unit to any vector compute unit - the locality of dense matmuls means that the computation of each tile only requires a small slice of both input matrices, so we only need to load those slices into shared memory; on the other hand, because GPU-to-GPU transfers are way slower, when we op-shard matmuls, we replicate the data that is needed. Sparse matmuls would need either more replication within each compute die, or more all-to-all internal bandwidth. This means spending way more die space on huge crossbars and routing. This would cost a lot of die space, though thankfully, the crossbars consume much less power than actual compute, so perhaps this could match dense in energy efficiency and not make thermals worse. It also seems very likely that once we create the interpretable GPT-1 (or 2, or 3) we will find that making everything unstructured sparse was overkill, and there are much more efficient pretraining constraints we can apply to models to 80/20 the interpretability. In general, a lot of my hope routes through learning things like this from the intermediate artifact (interpretable GPT-n). To be clear, it doesn't seem literally impossible that with great effort, we could create custom hardware, and vastly improve the optimization algorithms, etc, such that weight-sparse models could be vaguely close in performance to weight-dense models. It's plausible that with better optimization the win from arbitrary connectivity patterns might offset the hardware difficulties, and I could be overlooking something that would make the cost less than I expect. But this would require immense effort and investment to merely match current models, so it seems quite unrealistic compared to learning something from interpretable GPT-3 that helps us understand GPT-5.

kwillets 11mo ago

My last dive into matrix computations was years ago, but the need was the same back then. We could sparsify matrices pretty easily, but the infrastructure was lacking. Some things never change.

yvdriess 11mo ago
On the software side I can recommend https://github.com/DrTimothyAldenDavis/GraphBLAS https://github.com/DrTimothyAldenDavis/GraphBLAS It is hard to make a sparse linear algebra framework, but Tim Davis has been doing a great job collecting the various optimal algorithms I to a single framework that acts more like an algebra than a collection of kernels.