Daniel L. -K. Wong | Searcharxiv

arXiv2025

ZeroOS: A Universal Modular Library OS for zkVMs

zkVMs promise general-purpose verifiable computation through ISA-level compatibility with modern programs and toolchains. However, compatibility extends further than just the ISA; modern programs often cannot run or even compile without an operating system and libc. zkVMs attempt to address this by maintaining forks of language-specific runtimes and statically linking them into applications to create self-contained unikernels, but this ad-hoc approach leads to version hell and burdens verifiable applications (vApps) with an unnecessarily large trusted computing base. We solve this problem with ZeroOS, a modular library operating system (libOS) for vApp unikernels; vApp developers can use off-the-shelf toolchains to compile and link only the exact subset of the Linux ABI their vApp needs. Any zkVM team can easily leverage the ZeroOS ecosystem by writing a ZeroOS bootloader for their platform, resulting in a reduced maintainence burden and unifying the entire zkVM ecosystem with consolidated development and audit resources. ZeroOS is free and open-sourced at https://github.com/LayerZero-Labs/ZeroOS.

cs.OS↗

arXiv2019

Accelerating Deep Learning by Focusing on the Biggest Losers

This paper introduces Selective-Backprop, a technique that accelerates the training of deep neural networks (DNNs) by prioritizing examples with high loss at each iteration. Selective-Backprop uses the output of a training example's forward pass to decide whether to use that example to compute gradients and update parameters, or to skip immediately to the next example. By reducing the number of computationally-expensive backpropagation steps performed, Selective-Backprop accelerates training. Evaluation on CIFAR10, CIFAR100, and SVHN, across a variety of modern image models, shows that Selective-Backprop converges to target error rates up to 3.5x faster than with standard SGD and between 1.02--1.8x faster than a state-of-the-art importance sampling approach. Further acceleration of 26% can be achieved by using stale forward pass results for selection, thus also skipping forward passes of low priority examples.

cs.LG↗