[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  CRYPTO WELSHđ,đđĽ [@Hasdot313](/creator/twitter/Hasdot313) on x XXX followers Created: 2025-07-22 20:50:50 UTC Alright fam, Iâve been nerding out on @cysic_xyz HyperPlonk system lately this thing is seriously next-level when it comes to ZK proof generation. Let me break it down from a hardware perspective đ So HyperPlonk is Cysicâs custom take on the Plonk proof system but built on a Boolean hypercube instead of the typical polynomial basis. Whyâs that cool? Because it gets rid of heavy FFTs and brings in custom gates meaning you get more efficiency without a time penalty. Now if you're wondering how this plays out on the hardware side, it's all about three core computations: MSM (multi-scalar multiplication) MLE (multi-linear extension) SumCheck Together, these are the workhorses behind HyperPlonkâs performance. First off, HyperPlonk is a succinct non-interactive argument of knowledge (SNARK) and succinct is the keyword. The size of the proof and how fast it can be verified scale logarithmically with the statement size. Thatâs a big deal for real-world scalability. It builds on the IOP (interactive oracle proof) + commitment scheme model. Instead of reading full proofs, the verifier just queries them like getting info from an oracle. It supports commitment schemes like KZG, FRI, or Merkle trees to lock in those messages securely. Now, what makes HyperPlonk shine compared to vanilla Plonk is that hypercube structure. It avoids the bulky FFT steps and makes custom gate usage far easier a major win for hardware optimization. Looking at their current Rust implementation, you can see how tightly integrated each component is. In benchmarks using a 2š⸠constraint system: MSM takes ~43.5% of the compute time MLE takes ~28.5% SumCheck takes ~27.9% So yeah MSM is the monster here. MSM is all about computing a massive sum of elliptic curve points, like G = câGâ + câGâ + ... + câââGâââ This is expensive, and thatâs where Pippengerâs algorithm comes in it speeds things up by batching the math smartly. Cysicâs hardware design really flexes here. They built an FPGA based MSM pipeline fully pipelined, by the way so every component (adder, multiplier, accumulator) does its job in parallel. Modular multiplication is still the bottleneck, but theyâve optimized around it with smart queuing and architecture. All of this shows just how deep @cysic_xyz is going. Theyâre not just tweaking ZKPs theyâre rebuilding the foundation with hardware in mind. Thatâs a major unlock for scaling real applications using zero-knowledge tech. Genuinely impressed with the direction theyâre taking. If youâre building or just curious about next-gen zk hardware, keep watching this space. @cysic_xyz is setting the pace.  XXX engagements  **Related Topics** [hardware](/topic/hardware) [zk](/topic/zk) [Post Link](https://x.com/Hasdot313/status/1947761276316487876)
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CRYPTO WELSHđ,đđĽ @Hasdot313 on x XXX followers
Created: 2025-07-22 20:50:50 UTC
Alright fam, Iâve been nerding out on @cysic_xyz HyperPlonk system lately this thing is seriously next-level when it comes to ZK proof generation. Let me break it down from a hardware perspective đ
So HyperPlonk is Cysicâs custom take on the Plonk proof system but built on a Boolean hypercube instead of the typical polynomial basis. Whyâs that cool? Because it gets rid of heavy FFTs and brings in custom gates meaning you get more efficiency without a time penalty.
Now if you're wondering how this plays out on the hardware side, it's all about three core computations:
MSM (multi-scalar multiplication)
MLE (multi-linear extension)
SumCheck
Together, these are the workhorses behind HyperPlonkâs performance.
First off, HyperPlonk is a succinct non-interactive argument of knowledge (SNARK) and succinct is the keyword. The size of the proof and how fast it can be verified scale logarithmically with the statement size. Thatâs a big deal for real-world scalability.
It builds on the IOP (interactive oracle proof) + commitment scheme model. Instead of reading full proofs, the verifier just queries them like getting info from an oracle. It supports commitment schemes like KZG, FRI, or Merkle trees to lock in those messages securely.
Now, what makes HyperPlonk shine compared to vanilla Plonk is that hypercube structure. It avoids the bulky FFT steps and makes custom gate usage far easier a major win for hardware optimization.
Looking at their current Rust implementation, you can see how tightly integrated each component is.
In benchmarks using a 2š⸠constraint system:
MSM takes ~43.5% of the compute time
MLE takes ~28.5%
SumCheck takes ~27.9%
So yeah MSM is the monster here.
MSM is all about computing a massive sum of elliptic curve points, like G = câGâ + câGâ + ... + câââGâââ This is expensive, and thatâs where Pippengerâs algorithm comes in it speeds things up by batching the math smartly.
Cysicâs hardware design really flexes here. They built an FPGA based MSM pipeline fully pipelined, by the way so every component (adder, multiplier, accumulator) does its job in parallel. Modular multiplication is still the bottleneck, but theyâve optimized around it with smart queuing and architecture.
All of this shows just how deep @cysic_xyz is going. Theyâre not just tweaking ZKPs theyâre rebuilding the foundation with hardware in mind. Thatâs a major unlock for scaling real applications using zero-knowledge tech.
Genuinely impressed with the direction theyâre taking. If youâre building or just curious about next-gen zk hardware, keep watching this space. @cysic_xyz is setting the pace.
XXX engagements
