HePGA: A Heterogeneous Processing-in-Memory based GNN Training Accelerator
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915456700383232 |
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| author | Ogbogu, Chukwufumnanya Narang, Gaurav Joardar, Biresh Kumar Doppa, Janardhan Rao Chakrabarty, Krishnendu Pande, Partha Pratim |
| author_facet | Ogbogu, Chukwufumnanya Narang, Gaurav Joardar, Biresh Kumar Doppa, Janardhan Rao Chakrabarty, Krishnendu Pande, Partha Pratim |
| contents | Processing-In-Memory (PIM) architectures offer a promising approach to accelerate Graph Neural Network (GNN) training and inference. However, various PIM devices such as ReRAM, FeFET, PCM, MRAM, and SRAM exist, with each device offering unique trade-offs in terms of power, latency, area, and non-idealities. A heterogeneous manycore architecture enabled by 3D integration can combine multiple PIM devices on a single platform, to enable energy-efficient and high-performance GNN training. In this work, we propose a 3D heterogeneous PIM-based accelerator for GNN training referred to as HePGA. We leverage the unique characteristics of GNN layers and associated computing kernels to optimize their mapping on to different PIM devices as well as planar tiers. Our experimental analysis shows that HePGA outperforms existing PIM-based architectures by up to 3.8x and 6.8x in energy-efficiency (TOPS/W) and compute efficiency (TOPS/mm2) respectively, without sacrificing the GNN prediction accuracy. Finally, we demonstrate the applicability of HePGA to accelerate inferencing of emerging transformer models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HePGA: A Heterogeneous Processing-in-Memory based GNN Training Accelerator Ogbogu, Chukwufumnanya Narang, Gaurav Joardar, Biresh Kumar Doppa, Janardhan Rao Chakrabarty, Krishnendu Pande, Partha Pratim Emerging Technologies Hardware Architecture Machine Learning Processing-In-Memory (PIM) architectures offer a promising approach to accelerate Graph Neural Network (GNN) training and inference. However, various PIM devices such as ReRAM, FeFET, PCM, MRAM, and SRAM exist, with each device offering unique trade-offs in terms of power, latency, area, and non-idealities. A heterogeneous manycore architecture enabled by 3D integration can combine multiple PIM devices on a single platform, to enable energy-efficient and high-performance GNN training. In this work, we propose a 3D heterogeneous PIM-based accelerator for GNN training referred to as HePGA. We leverage the unique characteristics of GNN layers and associated computing kernels to optimize their mapping on to different PIM devices as well as planar tiers. Our experimental analysis shows that HePGA outperforms existing PIM-based architectures by up to 3.8x and 6.8x in energy-efficiency (TOPS/W) and compute efficiency (TOPS/mm2) respectively, without sacrificing the GNN prediction accuracy. Finally, we demonstrate the applicability of HePGA to accelerate inferencing of emerging transformer models. |
| title | HePGA: A Heterogeneous Processing-in-Memory based GNN Training Accelerator |
| topic | Emerging Technologies Hardware Architecture Machine Learning |
| url | https://arxiv.org/abs/2508.16011 |