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cs.LG2025
InstantFT: An FPGA-Based Runtime Subsecond Fine-tuning of CNN Models
Keisuke Sugiura, Hiroki Matsutani
Training deep neural networks (DNNs) requires significantly more computation and memory than inference, making runtime adaptation of DNNs challenging on resource-limited IoT platfo…
cs.LG2025
PointODE: Lightweight Point Cloud Learning with Neural Ordinary Differential Equations on Edge
Keisuke Sugiura, Mizuki Yasuda, Hiroki Matsutani
Embedded edge devices are often used as a computing platform to run real-world point cloud applications, but recent deep learning-based methods may not fit on such devices due to l…
cs.LG2025
ElasticZO: A Memory-Efficient On-Device Learning with Combined Zeroth- and First-Order Optimization
Keisuke Sugiura, Hiroki Matsutani
Zeroth-order (ZO) optimization is being recognized as a simple yet powerful alternative to standard backpropagation (BP)-based training. Notably, ZO optimization allows for trainin…