4 papers · 1 filter
MemFLoRA: Memory-Floor LoRA for CNN Adaptation at the Edge
Mehmet Emre Akbulut, Johannes Geier, Ulf Schlichtmann
On-device learning is necessary when the model encounters user-,sensor-, or environment-specific shifts after deployment. Although parameter-efficient fine-tuning (PEFT) methods, p…
KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs
Chuangtao Chen, Grace Li Zhang, Xunzhao Yin +3
Large Language Models (LLMs) rely heavily on Key-Value (KV) caching to minimize inference latency. However, standard KV caches are context-dependent: reusing a cached document in a…
OptINC: Optical In-Network-Computing for Scalable Distributed Learning
Sijie Fei, Grace Li Zhang, Bing Li +1
Distributed learning is widely used for training large models on large datasets by distributing parts of the model or dataset across multiple devices and aggregating the computed r…
A High-Level Compiler Integration Approach for Deep Learning Accelerators Supporting Abstraction and Optimization
Samira Ahmadifarsani, Daniel Mueller-Gritschneder, Ulf Schlichtmann
The growing adoption of domain-specific architectures in edge computing platforms for deep learning has highlighted the efficiency of hardware accelerators. However, integrating cu…