Bhasha-Rupantarika: Algorithm-Hardware Co-design approach for Multilingual Neural Machine Translation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913177268125696 |
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| author | Lokhande, Mukul Dewangan, Tanushree Mansoori, Mohd Sharik Chaudhari, Tejas J., Akarsh Lokhande, Damayanti Teman, Adam Vishvakarma, Santosh Kumar |
| author_facet | Lokhande, Mukul Dewangan, Tanushree Mansoori, Mohd Sharik Chaudhari, Tejas J., Akarsh Lokhande, Damayanti Teman, Adam Vishvakarma, Santosh Kumar |
| contents | This paper introduces Bhasha-Rupantarika, a light and efficient multilingual translation system tailored through algorithm-hardware codesign for resource-limited settings. The method investigates model deployment at sub-octet precision levels (FP8, INT8, INT4, and FP4), with experimental results indicating a 4.1x reduction in model size (FP4) and a 4.2x speedup in inference speed, which correlates with an increased throughput of 66 tokens/s (improvement by 4.8x). This underscores the importance of ultra-low precision quantization for real-time deployment in IoT devices using FPGA accelerators, achieving performance on par with expectations. Our evaluation covers bidirectional translation between Indian and international languages, showcasing its adaptability in low-resource linguistic contexts. The FPGA deployment demonstrated a 1.96x reduction in LUTs and a 1.65x decrease in FFs, resulting in a 2.2x enhancement in throughput compared to OPU and a 4.6x enhancement compared to HPTA. Overall, the evaluation provides a viable solution based on quantisation-aware translation along with hardware efficiency suitable for deployable multilingual AI systems. The entire codes [https://github.com/mukullokhande99/Bhasha-Rupantarika/] and dataset for reproducibility are publicly available, facilitating rapid integration and further development by researchers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10676 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bhasha-Rupantarika: Algorithm-Hardware Co-design approach for Multilingual Neural Machine Translation Lokhande, Mukul Dewangan, Tanushree Mansoori, Mohd Sharik Chaudhari, Tejas J., Akarsh Lokhande, Damayanti Teman, Adam Vishvakarma, Santosh Kumar Hardware Architecture Computation and Language Robotics Audio and Speech Processing This paper introduces Bhasha-Rupantarika, a light and efficient multilingual translation system tailored through algorithm-hardware codesign for resource-limited settings. The method investigates model deployment at sub-octet precision levels (FP8, INT8, INT4, and FP4), with experimental results indicating a 4.1x reduction in model size (FP4) and a 4.2x speedup in inference speed, which correlates with an increased throughput of 66 tokens/s (improvement by 4.8x). This underscores the importance of ultra-low precision quantization for real-time deployment in IoT devices using FPGA accelerators, achieving performance on par with expectations. Our evaluation covers bidirectional translation between Indian and international languages, showcasing its adaptability in low-resource linguistic contexts. The FPGA deployment demonstrated a 1.96x reduction in LUTs and a 1.65x decrease in FFs, resulting in a 2.2x enhancement in throughput compared to OPU and a 4.6x enhancement compared to HPTA. Overall, the evaluation provides a viable solution based on quantisation-aware translation along with hardware efficiency suitable for deployable multilingual AI systems. The entire codes [https://github.com/mukullokhande99/Bhasha-Rupantarika/] and dataset for reproducibility are publicly available, facilitating rapid integration and further development by researchers. |
| title | Bhasha-Rupantarika: Algorithm-Hardware Co-design approach for Multilingual Neural Machine Translation |
| topic | Hardware Architecture Computation and Language Robotics Audio and Speech Processing |
| url | https://arxiv.org/abs/2510.10676 |