Future intelligent robots are expected to process multiple inputs
simultaneously (such as image and audio data) and generate multiple outputs
accordingly (such as gender and emotion), similar to humans. Recent research
has shown that multi-input single-output (MISO) deep neural networks (DNN)
outperform traditional single-input single-output (SISO) models, representing a
significant step towards this goal. In this paper, we propose MIMONet, a novel
on-device multi-input multi-output (MIMO) DNN framework that achieves high
accuracy and on-device efficiency in terms of critical performance metrics such
as latency, energy, and memory usage. Leveraging existing SISO model
compression techniques, MIMONet develops a new deep-compression method that is
specifically tailored to MIMO models. This new method explores unique yet
non-trivial properties of the MIMO model, resulting in boosted accuracy and
on-device efficiency. Extensive experiments on three embedded platforms
commonly used in robotic systems, as well as a case study using the TurtleBot3
robot, demonstrate that MIMONet achieves higher accuracy and superior on-device
efficiency compared to state-of-the-art SISO and MISO models, as well as a
baseline MIMO model we constructed. Our evaluation highlights the real-world
applicability of MIMONet and its potential to significantly enhance the
performance of intelligent robotic systems.

提出了一种新的基于设备的多输入多输出（MIMO）深度神经网络（DNN）框架 MIMONet，通过利用现有的单输入单输出（SISO）模型压缩技术，针对 MIMO 模型开发了一种新的深度压缩方法，实现了高准确性和设备效率。在嵌入式平台和 TurtleBot3 机器人上的大量实验表明，MIMONet 比 SISO 和 MISO 模型以及基准 MIMO 模型在准确性和设备效率上都取得了更好的性能。

MIMONet：多输入多输出设备端深度学习

MIMONet: Multi-Input Multi-Output On-Device Deep Learning

Coarse-to-fine strategies have been extensively used for the architecture
design of single image deblurring networks. Conventional methods typically
stack sub-networks with multi-scale input images and gradually improve
sharpness of images from the bottom sub-network to the top sub-network,
yielding inevitably high computational costs. Toward a fast and accurate
deblurring network design, we revisit the coarse-to-fine strategy and present a
multi-input multi-output U-net (MIMO-UNet). The MIMO-UNet has three distinct
features. First, the single encoder of the MIMO-UNet takes multi-scale input
images to ease the difficulty of training. Second, the single decoder of the
MIMO-UNet outputs multiple deblurred images with different scales to mimic
multi-cascaded U-nets using a single U-shaped network. Last, asymmetric feature
fusion is introduced to merge multi-scale features in an efficient manner.
Extensive experiments on the GoPro and RealBlur datasets demonstrate that the
proposed network outperforms the state-of-the-art methods in terms of both
accuracy and computational complexity. Source code is available for research
purposes at this https URL

提出了一种多输入多输出的 U-net（MIMO-UNet）模型，采用粗到细的策略进行单幅图像去模糊网络的架构设计，其中引入了不对称特征融合技术，经实验证明该模型在精度和计算复杂度方面均优于现有方法。