Decentor-V: Lightweight ML Training on Low-Power RISC-V Edge Devices

• URL: http://arxiv.org/abs/2509.18118v1
• Date: Wed, 10 Sep 2025 09:33:54 GMT
• Title: Decentor-V: Lightweight ML Training on Low-Power RISC-V Edge Devices
• Abstract summary: L-SGD, a lightweight variant of gradient descent, has enabled neural network training on Arm Cortex-M Microcontroller Units (MCUs)<n>This work extends L-SGD to RISC-V-based MCUs, an open architecture that still lacks robust support for on-device training.<n>We introduce an 8-bit quantized version of L-SGD for RISC-V, which achieves nearly 4x reduction in memory usage and a 2.2x speedup in training time, with negligible accuracy degradation.
• Score: 2.2332974094443174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern IoT devices increasingly rely on machine learning solutions to process data locally. However, the lack of graphics processing units (GPUs) or dedicated accelerators on most platforms makes on-device training largely infeasible, often requiring cloud-based services to perform this task. This procedure often raises privacy-related concerns, and creates dependency on reliable and always-on connectivity. Federated Learning (FL) is a new trend that addresses these issues by enabling decentralized and collaborative training directly on devices, but it requires highly efficient optimization algorithms. L-SGD, a lightweight variant of stochastic gradient descent, has enabled neural network training on Arm Cortex-M Microcontroller Units (MCUs). This work extends L-SGD to RISC-V-based MCUs, an open and emerging architecture that still lacks robust support for on-device training. L-SGD was evaluated on both Arm and RISC-V platforms using 32-bit floating-point arithmetic, highlighting the performance impact of the absence of Floating-Point Units (FPUs) in RISC-V MCUs. To mitigate these limitations, we introduce an 8-bit quantized version of L-SGD for RISC-V, which achieves nearly 4x reduction in memory usage and a 2.2x speedup in training time, with negligible accuracy degradation.

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