12 papers
Bridging the Gap Between Hyperdimensional Computing and Kernel Methods via the Nyström Method
Quanling Zhao, Anthony Hitchcock Thomas, Ari Brin +2
Hyperdimensional computing (HDC) is an approach from the cognitive science literature for solving information processing tasks using data represented as high-dimensional random vec…
Compiler-Driven Approximation Tuning for Hyperdimensional Computing
Xavier Routh, Abdul Rafae Noor, Akash Kothari +4
As Moore's law reaches its physical and economic limits, domain-specific approaches are increasingly employed to accelerate machine learning workloads. Hyperdimensional Computing (…
ACRONYM: Accelerated Approximate Nearest Neighbor Search in Memory for Dynamic Vector Databases
Md Mizanur Rahaman Nayan, Tianqi Zhang, Flavio Ponzina +2
Vector database search with frequent updates is increasingly critical in applications such as retrieval augmented generation, recommendation systems, and large-scale embedding retr…
HAVEN: High-Bandwidth Flash Augmented Vector Engine for Large-Scale Approximate Nearest-Neighbor Search Acceleration
Po-Kai Hsu, Weihong Xu, Qunyou Liu +2
Retrieval-Augmented Generation (RAG) relies on large-scale Approximate Nearest Neighbor Search (ANNS) to retrieve semantically relevant context for large language models. Among ANN…
GenDRAM:Hardware-Software Co-Design of General Platform in DRAM
Tsung-Han Lu, Weihong Xu, Tajana Rosing
Dynamic programming (DP) algorithms, such as All-Pairs Shortest Path (APSP) and genomic sequence alignment, are fundamental to many scientific domains but are severely bottlenecked…
Divide and Learn: Multi-Objective Combinatorial Optimization at Scale
Esha Singh, Dongxia Wu, Chien-Yi Yang +3
Multi-objective combinatorial optimization seeks Pareto-optimal solutions over exponentially large discrete spaces, yet existing methods sacrifice generality, scalability, or theor…