Toward Attention-based TinyML: A Heterogeneous Accelerated Architecture and Automated Deployment Flow
<|reference_start|>Toward Attention-based TinyML: A Heterogeneous Accelerated Architecture and Automated Deployment Flow: One of the challenges for Tiny Machine Learning (tinyML) is keeping up with the evolution of Machine Learning models from Convolutional Neural Networks to Transformers. We address this by leveraging a heterogeneous architectural template coupling RISC-V processors with hardwired accelerators supported by an automated deployment flow. We demonstrate an Attention-based model in a tinyML power envelope with an octa-core cluster coupled with an accelerator for quantized Attention. Our deployment flow enables an end-to-end 8-bit MobileBERT, achieving leading-edge energy efficiency and throughput of 2960 GOp/J and 154 GOp/s at 32.5 Inf/s consuming 52.0 mW (0.65 V, 22 nm FD-SOI technology).<|reference_end|>
@article{wiese2024toward,
    title={Toward Attention-based TinyML: A Heterogeneous Accelerated Architecture
  and Automated Deployment Flow},
    author={Philip Wiese, Gamze .Islamou{g}lu, Moritz Scherer, Luka Macan,
  Victor J.B. Jung, Alessio Burrello, Francesco Conti, Luca Benini},
    journal={arXiv preprint arXiv:2408.02473},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02473},
    primaryClass={cs.AR cs.LG}
}