  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.
