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[PRE REVIEW]: FPGAI Engine for Neural Network Training and Inference #7427
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Five most similar historical JOSS papers: FreqAI: generalizing adaptive modeling for chaotic time-series market forecasts tfaip - a Generic and Powerful Research Framework for Deep Learning based on Tensorflow GraphNeT: Graph neural networks for neutrino telescope event reconstruction LINFA: a Python library for variational inference with normalizing flow and annealing High-performance neural population dynamics modeling enabled by scalable computational infrastructure |
@editorialbot query scope 👋 @umutcanaltin - I am going to run this one by our larger editorial board to ensure it meets our substantial scholarly effort requirements. I'll be back in touch as soon as a consensus has been reached. Thank you. |
Submission flagged for editorial review. |
@editorialbot reject 👋 @umutcanaltin - After review with our larger editorial board, we have decided to reject this submission on the grounds that it does not meet our requirements for substantial scholarly effort. This was due to the lower lines of code count and lack of documentation to test the engine and communicate how to extend the work. Thank you for submitting to JOSS. |
Paper rejected. |
Submitting author: @umutcanaltin (Umut Can Altin)
Repository: https://github.com/umutcanaltin/fpgai_compiler
Branch with paper.md (empty if default branch): main
Version: v1.0.0
Editor: Pending
Reviewers: Pending
Managing EiC: Chris Vernon
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