Blockchain
Polyhedra Explores Enhanced Zero-Knowledge Performance with GPU Acceleration for Expander System
Credit : cryptonews.net
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Polyhedra Community has unveiled breakthrough developments in zero-knowledge proof techniques, reaching over 2000x efficiency enhancements through GPU acceleration.
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The newest optimizations targeted on the Sumcheck protocol have taken benefit of the immense processing energy of GPUs, promising improved scalability and effectivity in blockchain functions.
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Based on Polyhedra lead researcher Tiancheng, “ZK has advanced. What began as a privacy-focused know-how is now a gateway to the scalability of blockchain,” highlighting the transformational potential of their improvements.
Polyhedra Community’s breakthrough in zero-knowledge proof techniques improves blockchain scalability and AI safety, doubtlessly remodeling the decentralized software panorama.
Breakthrough efficiency enchancment for zero-knowledge proofs
The workforce at Polyhedra has made vital progress within the subject zero-knowledge proofs (ZKPs)which achieves distinctive efficiency enchancment through the use of GPU acceleration. This innovation is especially mirrored of their testing Expanderproof systemwhich demonstrated unprecedented execution instances in comparison with conventional CPU strategies. When in comparison with the NVIDIA 4090 and H100 graphics playing cards, the outcomes spotlight the transformative affect of this know-how:
- For 134M ports with Mersenne extension:
- CPU time: 15.08 s
- NVIDIA 4090 Time: 41.0 ms
- NVIDIA H100 time: 16.4ms (919x enchancment)
- For 0.5B ports with Mersenne extension:
- CPU ran OOM*
- NVIDIA 4090 Time: 59.5 ms
- NVIDIA H100 Time: 1019x enchancment
These outcomes reveal how GPU acceleration can basically enhance the effectivity of ZK-proof techniques, paving the best way for broader adoption of those applied sciences in decentralized networks.
Functions of Zero-Information Proofs in AI safety and privateness
The intersection of ZKPs and synthetic intelligence seems to be an important growth space. Machine studying with out information (zkML) is a notable development that permits customers to confirm the accuracy of AI fashions with out revealing delicate coaching information. By integrating ZKPs, builders can create AI techniques that guarantee person privateness whereas sustaining transparency.
That is particularly important as AI techniques are more and more relied upon for important choices in industries akin to healthcare and finance. With zkML, stakeholders can be sure that their algorithms are usually not solely operationally sound, but additionally free from biases which are frequent in machine studying datasets.
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