2 citations · 2 across the 4 of their papers we have counts for
4 papers
Beacon: LLM Multi-Agent Driven Hardware Design Space Exploration for Heterogeneous Multi-Chiplet Deep Learning Accelerators
Boyu Li, Zongwei Zhu, Qianyue Cao +2
Heterogeneous multi-chiplet accelerators allow chiplets to be configured independently to better match different operator characteristics and improve inference efficiency. However,…
Compass: Co-Exploration of Mapping and Hardware for Heterogeneous Multi-Chiplet Accelerators Targeting LLM Inference Service Workloads
Boyu Li, Zongwei Zhu, Qianyue Cao +2
Large language models (LLMs) bring huge computational demands, which makes multi-chiplet accelerators that can integrate large-scale computing resources a powerful solution. Howeve…
DeltaFS: Pursuing Zero Update Overhead via Metadata-Enabled Delta Compression for Log-structured File System on Mobile Devices
Chao Wu, Cheng Ji, Geng Yuan +5
Data compression has been widely adopted to release mobile devices from intensive write pressure. Delta compression is particularly promising for its high compression efficacy over…
HADFL: Heterogeneity-aware Decentralized Federated Learning Framework
Jing Cao, Zirui Lian, Weihong Liu +2
Federated learning (FL) supports training models on geographically distributed devices. However, traditional FL systems adopt a centralized synchronous strategy, putting high commu…