8 papers
Polar Code Based Federated Learning: Convergence Analysis and Resource Allocation
Han Xiao, Wei Kang, Nan Liu
Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data; however, it faces significant communication bottlenecks and channe…
Balancing FP8 Computation Accuracy and Efficiency on Digital CIM via Shift-Aware On-the-fly Aligned-Mantissa Bitwidth Prediction
Liang Zhao, Kunming Shao, Zhipeng Liao +4
FP8 low-precision formats have gained significant adoption in Transformer inference and training. However, existing digital compute-in-memory (DCIM) architectures face challenges i…
DS-CIM: Digital Stochastic Computing-In-Memory Featuring Accurate OR-Accumulation via Sample Region Remapping for Edge AI Models
Kunming Shao, Liang Zhao, Jiangnan Yu +5
Stochastic computing (SC) offers hardware simplicity but suffers from low throughput, while high-throughput Digital Computing-in-Memory (DCIM) is bottlenecked by costly adder logic…
A Memory-Efficient Retrieval Architecture for RAG-Enabled Wearable Medical LLMs-Agents
Zhipeng Liao, Kunming Shao, Jiangnan Yu +5
With powerful and integrative large language models (LLMs), medical AI agents have demonstrated unique advantages in providing personalized medical consultations, continuous health…
DIRC-RAG: Accelerating Edge RAG with Robust High-Density and High-Loading-Bandwidth Digital In-ReRAM Computation
Kunming Shao, Zhipeng Liao, Jiangnan Yu +9
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieval but faces challenges on edge devices due to high storage, ene…
Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning
Xiaoyu Gan, Jingbo Jiang, Jingyang Zhu +3
Substantial efforts have been devoted to alleviating the impact of the long-tailed class distribution in federated learning. In this work, we observe an interesting phenomenon that…