Polyhedra has released a paradigm shift in Expander, a system used to power Zero Knowledge Machine Learning (ZKML), an important engine. This update offers CUDA 13.0 compatibility, shared memory optimization up to 1 TB/s bandwidth, and GPU-accelerated KZG commitment leading to an astonishing 9000 ZK proof per second on the M31Ext3 elliptic curve.
Why is compatibility with CUDA 13.0 important?
This update will allow for more current GPU-based features to be incorrect. This optimization allows ZKML systems to effectively transform interactive encryption protocols into non-interactions, enhancing both security and performance. CUDA 13.0 compatibility equips Polyhedra with the capabilities of future expanders and attracts more customers in the industrial sector who are willing to adopt the technology as a safe, fast and verifiable computing system.
1 TB/s bandwidth unlocked
Polyhedra also addressed one of ZKML's biggest bottlenecks accessing memory. This optimization demonstrates what the combination of elliptic curve encryption (ECC) and GPU acceleration can achieve when proof. Snarks and other zero-knowledge proof systems are based on the commitment of the KZG polynomial, but usually encounter computational bottlenecks.
ZKML's Backbone
All of these upgrades combine to bring the backbone to ZKML. This is not only faster, but more robust when it comes to the real world. The partnership announced by Polyhedra with Berkeley RDI is evidence of the academic and industrial traction of production-ready ZKML applications. As GPU acceleration, polynomial commitment, and cryptographic optimization progresses, ZKML is becoming the mainstream method of secure verification of AI.
