A Mixed-Signal Photonic SRAM-based High-Speed Energy-Efficient Photonic Tensor Core with Novel Electro-Optic ADC
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912455793311744 |
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| author | Kaiser, Md Abdullah-Al Sunder, Sugeet Jacob, Ajey P. Jaiswal, Akhilesh R. |
| author_facet | Kaiser, Md Abdullah-Al Sunder, Sugeet Jacob, Ajey P. Jaiswal, Akhilesh R. |
| contents | The rapid surge in data generated by Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) applications demands ultra-fast, scalable, and energy-efficient hardware, as traditional von Neumann architectures face significant latency and power challenges due to data transfer bottlenecks between memory and processing units. Furthermore, conventional electrical memory technologies are increasingly constrained by rising bitline and wordline capacitance, as well as the resistance of compact and long interconnects, as technology scales. In contrast, photonics-based in-memory computing systems offer substantial speed and energy improvements over traditional transistor-based systems, owing to their ultra-fast operating frequencies, low crosstalk, and high data bandwidth. Hence, we present a novel differential photonic SRAM (pSRAM) bitcell-augmented scalable mixed-signal multi-bit photonic tensor core, enabling high-speed, energy-efficient matrix multiplication operations using fabrication-friendly integrated photonic components. Additionally, we propose a novel 1-hot encoding electro-optic analog-to-digital converter (eoADC) architecture to convert the multiplication outputs into digital bitstreams, supporting processing in the electrical domain. Our designed photonic tensor core, utilizing GlobalFoundries' monolithic 45SPCLO technology node, achieves computation speeds of 4.10 tera-operations per second (TOPS) and a power efficiency of 3.02 TOPS/W. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22705 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Mixed-Signal Photonic SRAM-based High-Speed Energy-Efficient Photonic Tensor Core with Novel Electro-Optic ADC Kaiser, Md Abdullah-Al Sunder, Sugeet Jacob, Ajey P. Jaiswal, Akhilesh R. Optics Systems and Control The rapid surge in data generated by Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) applications demands ultra-fast, scalable, and energy-efficient hardware, as traditional von Neumann architectures face significant latency and power challenges due to data transfer bottlenecks between memory and processing units. Furthermore, conventional electrical memory technologies are increasingly constrained by rising bitline and wordline capacitance, as well as the resistance of compact and long interconnects, as technology scales. In contrast, photonics-based in-memory computing systems offer substantial speed and energy improvements over traditional transistor-based systems, owing to their ultra-fast operating frequencies, low crosstalk, and high data bandwidth. Hence, we present a novel differential photonic SRAM (pSRAM) bitcell-augmented scalable mixed-signal multi-bit photonic tensor core, enabling high-speed, energy-efficient matrix multiplication operations using fabrication-friendly integrated photonic components. Additionally, we propose a novel 1-hot encoding electro-optic analog-to-digital converter (eoADC) architecture to convert the multiplication outputs into digital bitstreams, supporting processing in the electrical domain. Our designed photonic tensor core, utilizing GlobalFoundries' monolithic 45SPCLO technology node, achieves computation speeds of 4.10 tera-operations per second (TOPS) and a power efficiency of 3.02 TOPS/W. |
| title | A Mixed-Signal Photonic SRAM-based High-Speed Energy-Efficient Photonic Tensor Core with Novel Electro-Optic ADC |
| topic | Optics Systems and Control |
| url | https://arxiv.org/abs/2506.22705 |