TinyVers: A 0.8-17 TOPS/W, 1.7 μW-20 mW, Tiny Versatile System-on-chip with State-Retentive eMRAM for Machine Learning Inference at the Extreme Edge.
Vikram JainJuan Sebastian P. GiraldoJaro De RooseBert BoonsLinyan MeiMarian VerhelstPublished in: VLSI Technology and Circuits (2022)
Keyphrases
- machine learning
- power consumption
- learning algorithm
- decision trees
- real time
- learning systems
- data mining
- power supply
- explanation based learning
- learning tasks
- text classification
- computer science
- active learning
- natural language processing
- computational intelligence
- pattern recognition
- knowledge acquisition
- support vector
- bayesian networks
- computer vision
- edge information
- design methodology
- artificial intelligence
- genetic algorithm