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20242026
most citedProxima: Near-storage Acceleration for Graph-based Approximate Nearest Neighbor Search in 3D NAND

2 citations · 2 across the 1 of their papers we have counts for

collaborators

7 papers

cs.AR20262 cited

Proxima: Near-storage Acceleration for Graph-based Approximate Nearest Neighbor Search in 3D NAND

Weihong Xu, Junwei Chen, Po-Kai Hsu +5

Approximate nearest neighbor search (ANNS) plays an indispensable role in a wide variety of applications, including recommendation systems, information retrieval, and semantic sear…

cs.AR2025

PIM-FW: Hardware-Software Co-Design of All-pairs Shortest Paths in DRAM

Tsung-Han Lu, Zheyu Li, Minxuan Zhou +1

All-pairs shortest paths (APSP) is a fundamental algorithm used for routing, logistics, and network analysis, but the cubic time complexity and heavy data movement of the canonical…

cs.AR2025

RAPID-Graph: Recursive All-Pairs Shortest Paths Using Processing-in-Memory for Dynamic Programming on Graphs

Yanru Chen, Zheyu Li, Keming Fan +5

All-pairs shortest paths (APSP) remains a major bottleneck for large-scale graph analytics, as data movement with cubic complexity overwhelms the bandwidth of conventional memory h…

cs.AR2025

Stratum: System-Hardware Co-Design with Tiered Monolithic 3D-Stackable DRAM for Efficient MoE Serving

Yue Pan, Zihan Xia, Po-Kai Hsu +8

As Large Language Models (LLMs) continue to evolve, Mixture of Experts (MoE) architecture has emerged as a prevailing design for achieving state-of-the-art performance across a wid…

cs.CR2025

Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption

Moontaha Nishat Chowdhury, André Bauer, Minxuan Zhou

In today's data-driven world, recommendation systems personalize user experiences across industries but rely on sensitive data, raising privacy concerns. Fully homomorphic encrypti…

cs.PL2025

HPVM-HDC: A Heterogeneous Programming System for Accelerating Hyperdimensional Computing

Russel Arbore, Xavier Routh, Abdul Rafae Noor +7

Hyperdimensional Computing (HDC), a technique inspired by cognitive models of computation, has been proposed as an efficient and robust alternative basis for machine learning. HDC…