and where is the Transformer library ;)
Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
31–40 of 58 posts
Re: Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
#32I know enough to understand this is interesting but sadly I don't know enough to understand how it works.
Re: Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
#33I love the name 'Kolmogorov'
Re: Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
#34I love the name 'Kolmogorov'
Because it's complex?
Re: Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
#35Has there been much exploration on how much benefit comes from precision in activation functions in KANs? There's a little niggle in the back of my head that maybe 90% of the benefit of KANs can be gained from a quite small variety of function shapes. Combined with input weighting, I almost feel you could have a representation that scales from a standard relu perceptron though KANs to something with weighted inputs a…
The benefit in KANs is interpretability, not expressivity. It's a structure that lends itself well to performing symbolic regression or other interpretable downstream tasks. This can make it better suited for scientific tasks, for example. You can easily replicate the practical performance of any KAN with an MLP, and it will train and run faster on modern architectures. This proposes a method it might be faster, but…
A key benefit of KANs is expressivity, as each layer is significantly more expressive than an MLP layer. This can be seen in our benchmarks: KAN networks need fewer layers than MLPs to match or beat their performance, even in software.
However, on GPUs, KAN implementations are far less efficient than MLPs: since B-spline locality is hard to exploit and lookup operations aren't as efficient. This is your original point about MLPs training and running faster on modern architectures: each KAN layer is more expressive, but its poor hardware efficiency makes it a net negative (at least for current approaches).
On FPGAs, LUT lookups are cheap, so KANs' expressive layers map to very hardware-efficient implementations, and the resulting networks are thus much more compact and efficient than equivalent MLPs.
On your second point: low precision is certainly viable for both inference and learning (as shown in our work), and quantization can even have a mild regularizing effect. However, task performance generally worsens with lower precision (here and across the literature): the use of low precision is fundamentally a result of the efficiency-performance tradeoff.
Re: Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
#36Earlier quoted context omitted.
The benefit in KANs is interpretability, not expressivity. It's a structure that lends itself well to performing symbolic regression or other interpretable downstream tasks. This can make it better suited for scientific tasks, for example. You can easily replicate the practical performance of any KAN with an MLP, and it will train and run faster on modern architectures. This proposes a method it might be faster, but…
A key benefit of KANs is expressivity, as each layer is significantly more expressive than an MLP layer. This can be seen in our benchmarks: KAN networks need fewer layers than MLPs to match or beat their performance, even in software. However, on GPUs, KAN implementations are far less efficient than MLPs: since B-spline locality is hard to exploit and lookup operations aren't as efficient. This is your original poin…
I generally agree with this rebuttal. Each KAN layer is more expressive on a per-layer basis, although there is a mapping to an MLP with more layers. With the current hardware implementations, yes, MLPs have an advantage overall. I can certainly respect the intention to make KANs faster, since it is a serious issue for more widespread adoption, and KANs certainly have their value.
I'm still very skeptical of arguing for KANs as an eventual replacement, like I've seen some papers on the subject argue. The reduced depth may not be an advantage. For example, higher depth for standard neural networks doesn't just add to expressivity, it actually induces spectral sparsity bias. KANs have a bias of their own, but it is different, and is sometimes better, sometimes worse, depending on the task. If increasing depth turns out to be important, KANs might remain less efficient overall.
Re: Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
#37Bit off topic but I have always wondered how is it decided whose names would come first in a paper. You mentioned you and Duc Hoang having equal contribution, so how did you both decide this? Was it that persons idea first or you were his roommate and owe him a beer? Coin toss? I never had an traditional college life. Always wondered about all this.
Re: Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
#38Has there been much exploration on how much benefit comes from precision in activation functions in KANs? There's a little niggle in the back of my head that maybe 90% of the benefit of KANs can be gained from a quite small variety of function shapes. Combined with input weighting, I almost feel you could have a representation that scales from a standard relu perceptron though KANs to something with weighted inputs a…
There is definitely a precision-performance tradeoff to consider. We explored this through ablation studies on bitwidth precision / resource usage in our work (Figure 6a in https://arxiv.org/pdf/2512.12850 , Figure 4 in https://arxiv.org/pdf/2602.02056 ). Further exploration into the mechanics here would definitely be useful. Regarding your point that "90% of the benefit of KANs can be gained from a small variety of…
Can I just say that this is extremely impressive work for a master's level thesis. Incredible work and I hope you manage to continue fulfilling your fantastic potential in your career!
Re: Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
#39Bit off topic but I have always wondered how is it decided whose names would come first in a paper. You mentioned you and Duc Hoang having equal contribution, so how did you both decide this? Was it that persons idea first or you were his roommate and owe him a beer? Coin toss? I never had an traditional college life. Always wondered about all this.
This differes between fields, sometimes even down to the niche subject. In my subfield (of computer science) it was strictly alphabetical by surname, and the idea was that either you contributed or not, and there is no gradient. In other fields it's 'main author' and everyone else, with the expectation that main author does more. Some have the group leader as the first name, or the 'big shot' is always first. My impression is that in medicine it is often a kind of ranking from 'most to least' main author.
Re: Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
#40Bit off topic but I have always wondered how is it decided whose names would come first in a paper. You mentioned you and Duc Hoang having equal contribution, so how did you both decide this? Was it that persons idea first or you were his roommate and owe him a beer? Coin toss? I never had an traditional college life. Always wondered about all this.
This differes between fields, sometimes even down to the niche subject. In my subfield (of computer science) it was strictly alphabetical by surname, and the idea was that either you contributed or not, and there is no gradient. In other fields it's 'main author' and everyone else, with the expectation that main author does more. Some have the group leader as the first name, or the 'big shot' is always first. My impr…
> the idea was that either you contributed or not, and there is no gradient
Is the work predefined,ie, how much will each person do?
> Some have the group leader as the first name, or the 'big shot' is always first. My impression is that in medicine it is often a kind of ranking from 'most to least' main author
Does this affect your standing in workplace or industry? When we read about papers in scientific journals, news, or even arxiv, they mostly refer to the work based on first name, like “potato-peeler et al”. Sure it’s for brevity But when you look at the authors, there maybe 10 ppl listed. I have always wondered how do they get recognised. Since Like you mentioned they have to contribute. If they contribute but their names get swallowed up within “et al”, how does someone know how much was their contribution?