HTVM: Efficient Neural Network Deployment On Heterogeneous TinyML Platforms
<|reference_start|>HTVM: Efficient Neural Network Deployment On Heterogeneous TinyML Platforms: Optimal deployment of deep neural networks (DNNs) on state-of-the-art Systems-on-Chips (SoCs) is crucial for tiny machine learning (TinyML) at the edge. The complexity of these SoCs makes deployment non-trivial, as they typically contain multiple heterogeneous compute cores with limited, programmer-managed memory to optimize latency and energy efficiency. We propose HTVM - a compiler that merges TVM with DORY to maximize the utilization of heterogeneous accelerators and minimize data movements. HTVM allows deploying the MLPerf(TM) Tiny suite on DIANA, an SoC with a RISC-V CPU, and digital and analog compute-in-memory AI accelerators, at 120x improved performance over plain TVM deployment.<|reference_end|>
@article{van delm2024htvm:,
    title={HTVM: Efficient Neural Network Deployment On Heterogeneous TinyML
  Platforms},
    author={Josse Van Delm, Maarten Vandersteegen, Alessio Burrello, Giuseppe
  Maria Sarda, Francesco Conti, Daniele Jahier Pagliari, Luca Benini, Marian
  Verhelst},
    journal={2023 60th ACM/IEEE Design Automation Conference (DAC), San
  Francisco, CA, USA, 2023, pp. 1-6},
    year={2024},
    doi={10.1109/DAC56929.2023.10247664},
    archivePrefix={arXiv},
    eprint={2406.07453},
    primaryClass={cs.PL cs.DC}
}