7 citations · 9 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
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…
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…
Fast-OverlaPIM: A Fast Overlap-driven Mapping Framework for Processing In-Memory Neural Network Acceleration
Xuan Wang, Minxuan Zhou, Tajana Rosing
Processing in-memory (PIM) is promising to accelerate neural networks (NNs) because it minimizes data movement and provides large computational parallelism. Similar to machine lear…
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…
FHEmem: A Processing In-Memory Accelerator for Fully Homomorphic Encryption
Minxuan Zhou, Yujin Nam, Pranav Gangwar +7
Fully Homomorphic Encryption (FHE) is a technique that allows arbitrary computations to be performed on encrypted data without the need for decryption, making it ideal for securing…