SGIRR: Sparse Graph Index Remapping for ReRAM Crossbar Operation Unit and Power Optimization

Resistive Random Access Memory (ReRAM) Crossbars are a promising process-in-memory technology to reduce enormous data movement overheads of large-scale graph processing between computation and memory units. ReRAM cells can combine with crossbar arrays to effectively accelerate graph processing, and...

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Veröffentlicht in:2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD) S. 1 - 7
Hauptverfasser: Wang, Cheng-Yuan, Chang, Yao-Wen, Chang, Yuan-Hao
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 29.10.2022
Schlagworte:
Benchmark testing
Clustering algorithms
Energy consumption
Indexes
Power dissipation
Resistive RAM
Visualization
ISSN:1558-2434
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Zusammenfassung:Resistive Random Access Memory (ReRAM) Crossbars are a promising process-in-memory technology to reduce enormous data movement overheads of large-scale graph processing between computation and memory units. ReRAM cells can combine with crossbar arrays to effectively accelerate graph processing, and partitioning ReRAM crossbar arrays into Operation Units (OUs) can further improve computation accuracy of ReRAM crossbars. The operation unit utilization was not optimized in previous work, incurring extra cost. This paper proposes a two-stage algorithm with a crossbar OU-aware scheme for sparse graph index remapping for ReRAM (SGIRR) crossbars, mitigating the influence of graph sparsity. In particular, this paper is the first to consider the given operation unit size with the remapping index algorithm, optimizing the operation unit and power dissipation. Experimental results show that our proposed algorithm reduces the utilization of crossbar OUs by 31.4%, improves the total OU block usage by 10.6%, and saves energy consumption by 17.2%, on average.
ISSN:1558-2434
DOI:10.1145/3508352.3549364