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RecFlash: Fast Recommendation Inference on NAND Flash-Based In-Storage Computing with Embedding-Optimized Data Mapping
- Baik, Jangho;
- Ji, Gisan;
- Shim, Wonbo;
- Ryu, Sungju
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0초록
Recommendation systems are widely used for personalized suggestions, but the growing amount of user data makes real-time processing difficult. NAND flash-based in-storage computing (ISC) is a favorable solution due to its large capacity, but random memory access patterns in recommendation systems cause underutilized internal bandwidth and degraded performance. This paper proposes RecFlash, a fast recommendation inference accelerator using a data remapping algorithm with NAND flash-based ISC. Experimental results show that it improves latency by up to 81 % over the existing ISC architectures. © 2025 IEEE.
키워드
data remapping; hardware accelerator; in-storage computing; NAND flash memory; Recommendation system
- 제목
- RecFlash: Fast Recommendation Inference on NAND Flash-Based In-Storage Computing with Embedding-Optimized Data Mapping
- 저자
- Baik, Jangho; Ji, Gisan; Shim, Wonbo; Ryu, Sungju
- 발행일
- 2025-10
- 유형
- Proceedings Paper
- 저널명
- Proceedings - 2025 21st IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2025