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20072024
most citedDeep Subdomain Adaptation Network for Image Classification

1.2k citations

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7 papers · 1 filter

cs.AR20241 cited

GDR-HGNN: A Heterogeneous Graph Neural Networks Accelerator Frontend with Graph Decoupling and Recoupling

Runzhen Xue, Mingyu Yan, Dengke Han +4

Heterogeneous Graph Neural Networks (HGNNs) have broadened the applicability of graph representation learning to heterogeneous graphs. However, the irregular memory access pattern…

cs.AR202410 cited

CIM-MLC: A Multi-level Compilation Stack for Computing-In-Memory Accelerators

Songyun Qu, Shixin Zhao, Bing Li +4

In recent years, various computing-in-memory (CIM) processors have been presented, showing superior performance over traditional architectures. To unleash the potential of various…

cs.AR202222 cited

Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals

Weidong Cao, Yilong Zhao, Adith Boloor +3

Processing-in-memory (PIM) architectures have demonstrated great potential in accelerating numerous deep learning tasks. Particularly, resistive random-access memory (RRAM) devices…

cs.AR2021

R2F: A Remote Retraining Framework for AIoT Processors with Computing Errors

Dawen Xu, Meng He, Cheng Liu +5

AIoT processors fabricated with newer technology nodes suffer rising soft errors due to the shrinking transistor sizes and lower power supply. Soft errors on the AIoT processors pa…

cs.AR2020

System measurement of Intel AEP Optane DIMM

Tianyue Lu, Haiyang Pan, Mingyu Chen

In recent years, memory wall has been a great performance bottleneck of computer system. To overcome it, Non-Volatile Main Memory (NVMM) technology has been discussed widely to pro…

cs.AR20201 cited

TCIM: Triangle Counting Acceleration With Processing-In-MRAM Architecture

Xueyan Wang, Jianlei Yang, Yinglin Zhao +7

Triangle counting (TC) is a fundamental problem in graph analysis and has found numerous applications, which motivates many TC acceleration solutions in the traditional computing p…