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New framework enables on-device AI training with float16 on RISC-V

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an open-source framework enabling on-device training of AI models using float16 precision on resource-constrained RISC-V single-core processors. This approach, which leverages standard RISC-V extensions like Zfh and Zvfh, can reduce memory footprint by approximately 50% with minimal impact on model performance. The framework also includes capabilities for layer-freezing to support transfer learning and fine-tuning scenarios, building upon the AIfES framework for embedded systems. AI

IMPACT Enables more efficient on-device AI model training, particularly for edge computing applications with limited resources.

RANK_REASON The item is an academic paper detailing a new hardware-software co-design for on-device AI training. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework enables on-device AI training with float16 on RISC-V

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The item is an academic paper detailing a new hardware-software co-design for on-device AI training. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Benjamin Hubinet, Pierre-Alain Moellic, Olivier Savry, Olivier Potin, Jean-Baptiste Rigaud ·

    Hardware-Software Co-Design for Float16 On-Device Training on RISC-V Single-Core

    arXiv:2607.21130v1 Announce Type: cross Abstract: By leveraging standard RISC-V extensions, namely Zfh (scalar float16) and Zvfh (vector float16), this work proposes an open-source framework to enable complete on-device training on resource-constrained RISC-V single-core. Our app…

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