Integrity report for XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Network on RISC-V based IoT End Nodes
A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.
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Paper page arXiv integrity.json bundle.json
Detector runs
Findings
No public integrity findings for this paper.
Signed record
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