The Next ZK Breakthrough: Why ZK Needs an XLA-Like Compiler
Most ZK discussions focus on new proving systems like Plonky3, Jolt, WHIR, and Hypercube. But what if the bigger breakthrough is the tooling underneath them?
In machine learning, XLA made it easier to run and optimize models across different hardware. In ZK, teams still rebuild many of the same proving pipelines, GPU code, and core building blocks from scratch. A shared compiler layer could help ZK systems reuse these optimizations, speeding up development and making proof systems easier to build and run.
Fractalyze’s approach is to bring the XLA/MLIR model to cryptography: write proving systems at a high level, compile them to any hardware target, and let the compiler handle optimization, scheduling, memory management, and hardware-specific code generation.
[ Summary ]
Format
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Short introduction and context setting to start.
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Silent reading of the summary before discussion begins.
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5-10 minute presentation to frame the key ideas.
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Moderator-led discussion with no promotions.
Session Lead & Moderators
Jooman Han is CEO of Fractalyze, a Seoul-based team building compiler infrastructure for verifiable computing and ZK. Before Fractalyze, he was CTO at A41, one of APAC’s leading staking infrastructure providers, with $2.5B in staked assets, 20+ supported protocols, and 1.8% of all ETH staked. A41 is now connected to Four Pillars through FP Validated, Four Pillars’ institutional validator and node-infrastructure arm. He previously worked on large-scale GPU and cloud infrastructure at Samsung Research, giving him a rare mix of ML systems, blockchain infra, and ZK engineering experience.
Brian is co-founder of Whitepaper Reading Clyb. He leads dev rel at Miden, previous dev rel research engineer at Polygon and prior to that co-founded communities in like EduDAO among others.
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