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7 papers · 1 filter
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…
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…
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…
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…
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…
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…