MIMONet: Multi-Input Multi-Output On-Device Deep Learning
Zexin Li Xiaoxi He Yufei Li Shahab Nikkhoo Wei Yang Lothar Thiele Cong Liu
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.