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