Demand for efficient onboard object detection is increasing due to its key
role in autonomous navigation. However, deploying object detection models such
as YOLO on resource constrained edge devices is challenging due to the high
computational requirements of such models. In this paper, an compressed object
detection model named Squeezed Edge YOLO is examined. This model is compressed
and optimized to kilobytes of parameters in order to fit onboard such edge
devices. To evaluate Squeezed Edge YOLO, two use cases - human and shape
detection - are used to show the model accuracy and performance. Moreover, the
model is deployed onboard a GAP8 processor with 8 RISC-V cores and an NVIDIA
Jetson Nano with 4GB of memory. Experimental results show Squeezed Edge YOLO
model size is optimized by a factor of 8x which leads to 76% improvements in
energy efficiency and 3.3x faster throughout.

压缩的物体检测模型 Squeezed Edge YOLO 被优化以适配资源受限的边缘设备，并通过两个用例展示了模型的准确性和性能。实验结果表明，Squeezed Edge YOLO 模型的尺寸优化了 8 倍，导致能效提高 76% 并且速度提高 3.3 倍。

压缩型边缘 YOLO：边缘设备上的目标检测

Squeezed Edge YOLO: Onboard Object Detection on Edge Devices

With the dramatically increased number of parameters in language models,
sparsity methods have received ever-increasing research focus to compress and
accelerate the models. While most research focuses on how to accurately retain
appropriate weights while maintaining the performance of the compressed model,
there are challenges in the computational overhead and memory footprint of
sparse training when compressing large-scale language models. To address this
problem, we propose a Parameter-efficient Sparse Training (PST) method to
reduce the number of trainable parameters during sparse-aware training in
downstream tasks. Specifically, we first combine the data-free and data-driven
criteria to efficiently and accurately measure the importance of weights. Then
we investigate the intrinsic redundancy of data-driven weight importance and
derive two obvious characteristics i.e., low-rankness and structuredness. Based
on that, two groups of small matrices are introduced to compute the data-driven
importance of weights, instead of using the original large importance score
matrix, which therefore makes the sparse training resource-efficient and
parameter-efficient. Experiments with diverse networks (i.e., BERT, RoBERTa and
GPT-2) on dozens of datasets demonstrate PST performs on par or better than
previous sparsity methods, despite only training a small number of parameters.
For instance, compared with previous sparsity methods, our PST only requires
1.5% trainable parameters to achieve comparable performance on BERT.

该研究提出了参数有效的稀疏训练 (PST) 方法，通过减少可训练参数的数量以使得稀疏训练资源有效并具有参数效益，从而解决了稀疏训练时计算开销和内存占用的问题，在 BERT，RoBERTa 和 GPT-2 等网络上得到了有效验证。