Author

Tianqi Zhang

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Preprint Oct 2026

Rosemary: Efficient and Adaptable In-Storage Acceleration for Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) enables computation on encrypted data and is promising for privacy-preserving computing. However, FHE deployment remains constrained due to massive computational, memory, and data-movement overheads. Existing acceleration strategies often overlook the storage I/O bottleneck caused by...

Xuan Wang, Tian-Qi Zhang, G. de Micheli et al. · 0 citations

Jul 2026

D-NOVA: In-Storage Retrieval Accelerator via Dual-Bound 3D NAND-Optimized Similarity Search with Vector Adaptation

D-NOVA is a hardware-software co-designed in-storage retrieval accelerator that executes an inverted file (IVF)-based hierarchical retrieval pipeline by deeply embedding the search functionality directly into the NAND memory array, and introduces a lightweight contrastive adapter that maps embedding vectors into a DTS-...

Chang Eun Song, Sumukh Pinge, Tian-Qi Zhang et al. · arXiv.org · 0 citations

#artificial intelligence Preprint Sep 2026

Superposed Latent Autoencoder

The Superposed Latent Autoencoder (SLAE) is introduced, which preserves high-capacity latent representations while sharing storage through learned superposition, and suggests a new principle for representation compression: instead of making every latent smaller, keep representations wide and let them share memory.

Quan-Ling Zhao, Jia-Ying Yang, Tian-Qi Zhang et al. · 0 citations