3 papers
cs.LG2026
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…
cs.AR2026
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…
cs.AR2026
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…