The advancement of sophisticated artificial intelligence (AI) algorithms has
led to a notable increase in energy usage and carbon dioxide emissions,
intensifying concerns about climate change. This growing problem has brought
the environmental sustainability of AI technologies to the forefront,
especially as they expand across various sectors. In response to these
challenges, there is an urgent need for the development of sustainable AI
solutions. These solutions must focus on energy-efficient embedded systems that
are capable of handling diverse data types even in environments with limited
resources, thereby ensuring both technological progress and environmental
responsibility. Integrating complementary multimodal data into tiny machine
learning models for edge devices is challenging due to increased complexity,
latency, and power consumption. This work introduces TinyM$^2$Net-V3, a system
that processes different modalities of complementary data, designs deep neural
network (DNN) models, and employs model compression techniques including
knowledge distillation and low bit-width quantization with memory-aware
considerations to fit models within lower memory hierarchy levels, reducing
latency and enhancing energy efficiency on resource-constrained devices. We
evaluated TinyM$^2$Net-V3 in two multimodal case studies: COVID-19 detection
using cough, speech, and breathing audios, and pose classification from depth
and thermal images. With tiny inference models (6 KB and 58 KB), we achieved
92.95% and 90.7% accuracies, respectively. Our tiny machine learning models,
deployed on resource limited hardware, demonstrated low latencies within
milliseconds and very high power efficiency.

由于人工智能算法的普及和应用，能源使用和二氧化碳排放显著增加，加剧了对气候变化的担忧。为了解决这一问题，我们需要开发可持续的人工智能解决方案，特别是能耗低、适用于环境资源有限的嵌入式系统。本文介绍了 TinyM^2Net-V3，它是一个处理多模态数据的系统，设计了深度神经网络模型，并采用知识蒸馏和低位宽量化等模型压缩技术，以适应较低级别的内存层次，降低延迟，提高能源效率。我们通过 COVID-19 的咳嗽、语音和呼吸音频检测以及深度和热图像姿势分类两个多模态案例研究对 TinyM^2Net-V3 进行了评估，取得了 92.95% 和 90.7% 的准确率。我们的小型机器学习模型在资源有限的硬件上展示出低延迟和很高的功耗效率。