A Collaborative PIM Computing Optimization Framework for Multi-Tenant DNN
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929453968392192 |
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| author | Li, Bojing Zhong, Duo Chen, Xiang Liu, Chenchen |
| author_facet | Li, Bojing Zhong, Duo Chen, Xiang Liu, Chenchen |
| contents | Modern Artificial Intelligence (AI) applications are increasingly utilizing multi-tenant deep neural networks (DNNs), which lead to a significant rise in computing complexity and the need for computing parallelism. ReRAM-based processing-in-memory (PIM) computing, with its high density and low power consumption characteristics, holds promising potential for supporting the deployment of multi-tenant DNNs. However, direct deployment of complex multi-tenant DNNs on exsiting ReRAM-based PIM designs poses challenges. Resource contention among different tenants can result in sever under-utilization of on-chip computing resources. Moreover, area-intensive operators and computation-intensive operators require excessively large on-chip areas and long processing times, leading to high overall latency during parallel computing. To address these challenges, we propose a novel ReRAM-based in-memory computing framework that enables efficient deployment of multi-tenant DNNs on ReRAM-based PIM designs. Our approach tackles the resource contention problems by iteratively partitioning the PIM hardware at tenant level. In addition, we construct a fine-grained reconstructed processing pipeline at the operator level to handle area-intensive operators. Compared to the direct deployments on traditional ReRAM-based PIM designs, our proposed PIM computing framework achieves significant improvements in speed (ranges from 1.75x to 60.43x) and energy(up to 1.89x). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04812 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Collaborative PIM Computing Optimization Framework for Multi-Tenant DNN Li, Bojing Zhong, Duo Chen, Xiang Liu, Chenchen Emerging Technologies Artificial Intelligence Modern Artificial Intelligence (AI) applications are increasingly utilizing multi-tenant deep neural networks (DNNs), which lead to a significant rise in computing complexity and the need for computing parallelism. ReRAM-based processing-in-memory (PIM) computing, with its high density and low power consumption characteristics, holds promising potential for supporting the deployment of multi-tenant DNNs. However, direct deployment of complex multi-tenant DNNs on exsiting ReRAM-based PIM designs poses challenges. Resource contention among different tenants can result in sever under-utilization of on-chip computing resources. Moreover, area-intensive operators and computation-intensive operators require excessively large on-chip areas and long processing times, leading to high overall latency during parallel computing. To address these challenges, we propose a novel ReRAM-based in-memory computing framework that enables efficient deployment of multi-tenant DNNs on ReRAM-based PIM designs. Our approach tackles the resource contention problems by iteratively partitioning the PIM hardware at tenant level. In addition, we construct a fine-grained reconstructed processing pipeline at the operator level to handle area-intensive operators. Compared to the direct deployments on traditional ReRAM-based PIM designs, our proposed PIM computing framework achieves significant improvements in speed (ranges from 1.75x to 60.43x) and energy(up to 1.89x). |
| title | A Collaborative PIM Computing Optimization Framework for Multi-Tenant DNN |
| topic | Emerging Technologies Artificial Intelligence |
| url | https://arxiv.org/abs/2408.04812 |