2 citations · 2 across the 8 of their papers we have counts for
4 papers · 1 filter
Mitigating the Bandwidth Wall via Data-Streaming System-Accelerator Co-Design
Qunyou Liu, Marina Zapater, David Atienza
Transformers have revolutionized AI in natural language processing and computer vision, but their large computation and memory demands pose major challenges for hardware accelerati…
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…
MatrixFlow: System-Accelerator co-design for high-performance transformer applications
Qunyou Liu, Marina Zapater, David Atienza
Transformers are central to advances in artificial intelligence (AI), excelling in fields ranging from computer vision to natural language processing. Despite their success, their…
Gem5-AcceSys: Enabling System-Level Exploration of Standard Interconnects for Novel Accelerators
Qunyou Liu, Marina Zapater, David Atienza
The growing demand for efficient, high-performance processing in machine learning (ML) and image processing has made hardware accelerators, such as GPUs and Data Streaming Accelera…