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2026-10-21, Week 43
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Virual Oral Question and Answer
IEEE/ICACT20260254 Question.2
Questioner: mrajhans@paloaltonetworks.com    2026-02-11 오전 9:13:10 		IEEE/ICACT20260254 Answer.2
Answer by Auhor qga0809@chungbuk.ac.kr   2026-02-11 오전 9:13:10
How does the proposed MLP-based accelerator compare to LSTM-based approaches in terms of prediction accuracy and robustness, and what is the quantitative trade-off between the achieved latency gains and any loss in modeling temporal dependencies? Thank you for the question. Our current work focuses on demonstrating FPGA hardware acceleration feasibility rather than a comprehensive accuracy benchmark against LSTM. The key design rationale is that our destination prediction task is a contextual feature-based classification where temporal patterns are explicitly encoded as input features, making recurrent structures unnecessary. The 99.1% prediction confidence and 68.53× speedup over ARM software validate this approach, and we plan to provide a detailed accuracy comparison in future work. Thank you.

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