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Found 6 papers, 2 papers with code
Benchmarking Ultra-Low-Power $μ$NPUs
Our framework provides a foundation for further evaluation of $\mu$NPU platforms alongside valuable insights for both hardware designers and software developers in this rapidly evolving space.
Towards Low-Energy Adaptive Personalization for Resource-Constrained Devices
For each type, we fine-tune different blocks of the model to achieve optimal performance with reduced energy costs.
Low-Energy On-Device Personalization for MCUs
During local inference, MicroT reduces energy cost by 14. 17% compared to TTL across two boards and two datasets, highlighting its suitability for long-term use on energy-aware resource-constrained MCUs.
Towards Machine Learning and Inference for Resource-constrained MCUs
The results demonstrate that, our pipeline can achieve 97. 78% accuracy with 483. 82 KB Flash, 70. 32 KB RAM, 118 ms runtime, 4. 83 mW power, and 0. 57 mJ energy consumption on MCUs, reducing by 64. 17%, 12. 31%, 52. 42%, 63. 74%, and 82. 67%, compared to the baseline.
Information Theory Inspired Pattern Analysis for Time-series Data
For applications with stochastic state transitions, features are developed based on Shannon's entropy of Markov chains, entropy rates of Markov chains, entropy production of Markov chains, and von Neumann entropy of Markov chains.
Using Entropy Measures for Monitoring the Evolution of Activity Patterns
The study uses Internet of Things (IoT) enabled solutions for continuous monitoring of in-home activity, sleep, and physiology to develop care and early intervention solutions to support people living with dementia (PLWD) in their own homes.
