Jul 22, 2020 · We report the trade-off between memory footprint, latency, and accuracy for learning a new class with Latent Replay CL when targeting an image classification ...

In this paper, we present a Hardware/Software platform design to run for the first time Continual Learning (CL) algorithms at the extreme-edge on a ...

... To perform it at the extreme edge, we can re-train only a portion of the network by exploiting stored feature maps in place of the full training dataset.

Jul 22, 2020 · We report the trade-off between memory footprint, latency, and accuracy ... Trade-Offs for Continual Learning on a RISC-V Extreme-Edge Node.

Memory-latency-accuracy trade-offs for continual learning on a risc-v extreme-edge node. L Ravaglia, M Rusci, A Capotondi, F Conti, L Pellegrini, V Lomonaco, ..

Jul 27, 2024 · Memory-Latency-Accuracy Trade-Offs for Continual Learning on a RISC-V Extreme-Edge Node. SiPS 2020: 1-6. [i6]. view. electronic edition @ arxiv ...

A Mixed-Precision RISC-V Processor for Extreme-Edge DNN Inference ... Memory-Latency-Accuracy Trade-offs for Continual Learning on a RISC-V Extreme-Edge Node

Co-authors ; Memory-latency-accuracy trade-offs for continual learning on a risc-v extreme-edge node. L Ravaglia, M Rusci, A Capotondi, F Conti, L Pellegrini, V ...

Memory-Latency-Accuracy Trade-offs for Continual Learning on a RISC-V Extreme-Edge Node. arXiv. 2020 | Other. EID: 2-s2.0-85095519632. Part of ISSN: 23318422.

Memory-Latency-Accuracy Trade-Offs for Continual Learning on a RISC-V Extreme-Edge Node · Computer Science, Engineering. 2020 IEEE Workshop on Signal Processing ...