A Multicast-Capable AXI Crossbar for Many-core Machine Learning Accelerators
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908639442239488 |
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| author | Colagrande, Luca Benini, Luca |
| author_facet | Colagrande, Luca Benini, Luca |
| contents | To keep up with the growing computational requirements of machine learning workloads, many-core accelerators integrate an ever-increasing number of processing elements, putting the efficiency of memory and interconnect subsystems to the test. In this work, we present the design of a multicast-capable AXI crossbar, with the goal of enhancing data movement efficiency in massively parallel machine learning accelerators. We propose a lightweight, yet flexible, multicast implementation, with a modest area and timing overhead (12% and 6% respectively) even on the largest physically-implementable 16-to-16 AXI crossbar. To demonstrate the flexibility and end-to-end benefits of our design, we integrate our extension into an open-source 288-core accelerator. We report tangible performance improvements on a key computational kernel for machine learning workloads, matrix multiplication, measuring a 29% speedup on our reference system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19215 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multicast-Capable AXI Crossbar for Many-core Machine Learning Accelerators Colagrande, Luca Benini, Luca Hardware Architecture To keep up with the growing computational requirements of machine learning workloads, many-core accelerators integrate an ever-increasing number of processing elements, putting the efficiency of memory and interconnect subsystems to the test. In this work, we present the design of a multicast-capable AXI crossbar, with the goal of enhancing data movement efficiency in massively parallel machine learning accelerators. We propose a lightweight, yet flexible, multicast implementation, with a modest area and timing overhead (12% and 6% respectively) even on the largest physically-implementable 16-to-16 AXI crossbar. To demonstrate the flexibility and end-to-end benefits of our design, we integrate our extension into an open-source 288-core accelerator. We report tangible performance improvements on a key computational kernel for machine learning workloads, matrix multiplication, measuring a 29% speedup on our reference system. |
| title | A Multicast-Capable AXI Crossbar for Many-core Machine Learning Accelerators |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2502.19215 |