Pith. sign in

REVIEW 1 cited by

RTNeural: Fast Neural Inferencing for Real-Time Systems

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2106.03037 v1 pith:TMLYW4GC submitted 2021-06-06 eess.AS

Jatin Chowdhury This is my paper · ORCID
classification eess.AS
keywords inferencingneuralrtneurallibraryreal-timesystemsadditionalcomparisons
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

RTNeural is a neural inferencing library written in C++. RTNeural is designed to be used in systems with hard real-time constraints, with additional emphasis on speed, flexibility, size, and convenience. The motivation and design of the library are described, as well as real-world use-cases, and performance comparisons with other neural inferencing libraries.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ANIRA: An Architecture for Neural Network Inference in Real-Time Audio Applications

    cs.SD 2025-06 conditional novelty 6.0 of 10

    Anira, a new library for real-time audio neural network inference, is benchmarked across three engines, finding ONNX Runtime fastest for stateless models and LibTorch fastest for stateful models.

Pith tools