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| Autori principali: | , , , , , |
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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2604.12278 |
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| _version_ | 1866917406167793664 |
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| author | Gong, Hailong Zhang, Haibo Barnard, Amanda S. Hassan, Mahbub Woolley, Matt Buyya, Rajkumar |
| author_facet | Gong, Hailong Zhang, Haibo Barnard, Amanda S. Hassan, Mahbub Woolley, Matt Buyya, Rajkumar |
| contents | Matrix multiplication is a fundamental kernel in large-scale artificial intelligence and scientific computing, but its performance on conventional electronic accelerators is increasingly constrained by memory bandwidth and energy efficiency. Photonic computing offers a promising alternative due to its ultra-high bandwidth, massive parallelism, and low power dissipation. However, most existing photonic systems are limited to low-precision computation because of analog optical modulation constraints and noise accumulation, which restricts their applicability in precision-critical workloads. To address this limitation, we propose LightMat-HP, a hybrid photonic-electronic computing system that enables end-to-end acceleration of general matrix multiplication with configurable computational precision. LightMat-HP adopts block floating-point (BFP) arithmetic to reduce computational complexity while enabling flexible precision-performance tradeoffs. To overcome the precision limitations of photonic devices, we propose a slicing-based photonic multiplication scheme that exploits the high accuracy of low bit-width photonic multiplication in combination with digital accumulation to achieve high-precision mantissa multiplication. A tile-based matrix multiplication dataflow is further designed to support matrices of arbitrary sizes. We experimentally validate LightMat-HP on a photonic computing prototype and evaluate its performance through large-scale simulations. The results demonstrate that LightMat-HP outperforms FPGA, GPU, and a state-of-the-art photonic accelerator across throughput, latency, and energy efficiency, particularly for small- and medium-sized matrix multiplications, owing to its highly parallel photonic architecture, efficient data movement, and slice-based BFP arithmetic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12278 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LightMat-HP: A Photonic-Electronic System for Accelerating General Matrix Multiplication With Configurable Precision Gong, Hailong Zhang, Haibo Barnard, Amanda S. Hassan, Mahbub Woolley, Matt Buyya, Rajkumar Emerging Technologies Matrix multiplication is a fundamental kernel in large-scale artificial intelligence and scientific computing, but its performance on conventional electronic accelerators is increasingly constrained by memory bandwidth and energy efficiency. Photonic computing offers a promising alternative due to its ultra-high bandwidth, massive parallelism, and low power dissipation. However, most existing photonic systems are limited to low-precision computation because of analog optical modulation constraints and noise accumulation, which restricts their applicability in precision-critical workloads. To address this limitation, we propose LightMat-HP, a hybrid photonic-electronic computing system that enables end-to-end acceleration of general matrix multiplication with configurable computational precision. LightMat-HP adopts block floating-point (BFP) arithmetic to reduce computational complexity while enabling flexible precision-performance tradeoffs. To overcome the precision limitations of photonic devices, we propose a slicing-based photonic multiplication scheme that exploits the high accuracy of low bit-width photonic multiplication in combination with digital accumulation to achieve high-precision mantissa multiplication. A tile-based matrix multiplication dataflow is further designed to support matrices of arbitrary sizes. We experimentally validate LightMat-HP on a photonic computing prototype and evaluate its performance through large-scale simulations. The results demonstrate that LightMat-HP outperforms FPGA, GPU, and a state-of-the-art photonic accelerator across throughput, latency, and energy efficiency, particularly for small- and medium-sized matrix multiplications, owing to its highly parallel photonic architecture, efficient data movement, and slice-based BFP arithmetic. |
| title | LightMat-HP: A Photonic-Electronic System for Accelerating General Matrix Multiplication With Configurable Precision |
| topic | Emerging Technologies |
| url | https://arxiv.org/abs/2604.12278 |