Author

Kecheng Huang

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Aug 2026

Hope: Differentiated Backbone-Expansion ANNS Updates via In-Storage Computing

Graph-based Approximate Nearest Neighbor Search (ANNS) has become fundamental to modern data-intensive applications, yet supporting efficient vector updates while maintaining index quality remains a critical challenge. Existing approaches face a tough dilemma: extensive reconstruction ensures connectivity but incurs unpredictable overhead, while restricting update scope improves efficiency but degrades search accuracy. In this paper, we reveal that graph-based ANNS indexes inherently comprise backbone nodes that dictate global navigation capability and expansion nodes that provide local refinement. This insight motivates Hope, a Host-CSD (Computational Storage Device) cooptimized update framework that optimizes both index quality and update efficiency with a differentiated update design. For efficient vector categorization, we propose an in-storage sketcher that leverages per-dimension correlation with the bitmap-based flip mechanism to dynamically identify backbone and expansion vectors with minimal overhead. For asymmetric update handling, Hope employs the host-CPU for computation-intensive backbone updates with multi-hop neighbor gathering to preserve connectivity, while delegating I/O-intensive expansion updates to in-storage computing for immediate local manipulations. Our evaluation demonstrates that Hope achieves superior update efficiency and index quality compared to state-of-the-art baselines, providing stable performance for both vector updates and ANNS queries in dynamic environments.

Kecheng Huang, Jiaxian Chen, Chenlin Ma et al. · IEEE Non-Volatile Memory Sys... · 0 citations

Preprint Jul 2026

TF-Engram: A Train-Free Engram with SSD-Backed Memory for Large Language Models

Large Language Models (LLMs) store factual knowledge and domain-specific patterns implicitly in dense Transformer parameters, making knowledge expansion costly through pretraining, fine-tuning, retrieval augmentation, or longer contexts. Engram-style memory offers a compact hidden-state injection pathway, but existing GPU-resident designs often rely on hash-based compression, causing unrelated phrases to collide in shared slots and weakening phrase-level semantic fidelity. We present TF-Engram, a train-free Engram system that constructs phrase-specific semantic memory offline from external corpora, stores large memory tables across a GPU--DRAM--SSD hierarchy, and uses Early-Exit Guided Predictive Prefetching to hide external-memory latency during autoregressive decoding. On Qwen3-0.6B, TF-Engram improves the average downstream score from 57.6 to 59.4, outperforming both the frozen backbone and a parameter-matched LoRA baseline. System evaluation shows that large TF-Engram tables can be built with moderate offline cost, SSD-backed storage substantially reduces GPU memory demand, and predictive prefetching recovers much of the throughput loss caused by external memory access. These results demonstrate that static phrase memory can be integrated into LLM inference as a scalable, train-free, and low-overhead system component.

Yutang Ma, Kecheng Huang, Xikun Jiang et al. · 0 citations