This paper deploys and explores variants of TinyissimoYOLO, a highly flexible
and fully quantized ultra-lightweight object detection network designed for
edge systems with a power envelope of a few milliwatts. With experimental
measurements, we present a comprehensive characterization of the network's
detection performance, exploring the impact of various parameters, including
input resolution, number of object classes, and hidden layer adjustments. We
deploy variants of TinyissimoYOLO on state-of-the-art ultra-low-power extreme
edge platforms, presenting an in-depth a comparison on latency, energy
efficiency, and their ability to efficiently parallelize the workload. In
particular, the paper presents a comparison between a novel parallel RISC-V
processor (GAP9 from Greenwaves) with and without use of its on-chip hardware
accelerator, an ARM Cortex-M7 core (STM32H7 from ST Microelectronics), two ARM
Cortex-M4 cores (STM32L4 from STM and Apollo4b from Ambiq), and a multi-core
platform with a CNN hardware accelerator (Analog Devices MAX78000).
Experimental results show that the GAP9's hardware accelerator achieves the
lowest inference latency and energy at 2.12ms and 150uJ respectively, which is
around 2x faster and 20% more efficient than the next best platform, the
MAX78000. The hardware accelerator of GAP9 can even run an increased resolution
version of TinyissimoYOLO with 112x112 pixels and 10 detection classes within
3.2ms, consuming 245uJ. To showcase the competitiveness of a versatile
general-purpose system we also deployed and profiled a multi-core
implementation on GAP9 at different operating points, achieving 11.3ms with the
lowest-latency and 490uJ with the most energy-efficient configuration. With
this paper, we demonstrate the suitability and flexibility of TinyissimoYOLO on
state-of-the-art detection datasets for real-time ultra-low-power edge
inference.

本文介绍了一种超轻量级物体检测网络 TinyissimoYOLO，旨在为功耗为几毫瓦的边缘系统提供高度灵活且完全量化的解决方案，实验结果表明，该模型在不同参数下都表现出较好的性能，并且在多种超低功耗平台上展现出更高的能效和更低的推理延迟。

适用于超低功耗边缘系统的灵活且全量化的超轻量级 TinyissimoYOLO

Flexible and Fully Quantized Ultra-Lightweight TinyissimoYOLO for  Ultra-Low-Power Edge Systems

Many emerging AI applications request distributed machine learning (ML) among
edge systems (e.g., IoT devices and PCs at the edge of the Internet), where
data cannot be uploaded to a central venue for model training, due to their
large volumes and/or security/privacy concerns. Edge devices are intrinsically
heterogeneous in computing capacity, posing significant challenges to parameter
synchronization for parallel training with the parameter server (PS)
architecture. This paper proposes ADSP, a parameter synchronization scheme for
distributed machine learning (ML) with heterogeneous edge systems. Eliminating
the significant waiting time occurring with existing parameter synchronization
models, the core idea of ADSP is to let faster edge devices continue training,
while committing their model updates at strategically decided intervals. We
design algorithms that decide time points for each worker to commit its model
update, and ensure not only global model convergence but also faster
convergence. Our testbed implementation and experiments show that ADSP
outperforms existing parameter synchronization models significantly in terms of
ML model convergence time, scalability and adaptability to large heterogeneity.

提出 ADSP 参数同步方案，解决了分布式机器学习中参数同步与边缘设备的异构性问题，通过策略性地决定每个工作节点提交模型更新的时间点，不仅确保了全局模型收敛，而且加快了收敛速度。