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ReaLPrune: ReRAM Crossbar-aware Lottery Ticket Pruned CNNs
[article]
2022
arXiv
pre-print
Training machine learning (ML) models at the edge (on-chip training on end user devices) can address many pressing challenges including data privacy/security, increase the accessibility of ML applications to different parts of the world by reducing the dependence on the communication fabric and the cloud infrastructure, and meet the real-time requirements of AR/VR applications. However, existing edge platforms do not have sufficient computing capabilities to support complex ML tasks such as
arXiv:2111.09272v3
fatcat:3dk5vkd6vrar7hzufdyvlfhtwu