Bioinspired and Low-Power 2D Machine Vision with Adaptive Machine Learning and Forgetting.
ACS Nano
; 16(12): 20010-20020, 2022 12 27.
Article
em En
| MEDLINE
| ID: mdl-36305614
ABSTRACT
Natural intelligence has many dimensions, with some of its most important manifestations being tied to learning about the environment and making behavioral changes. In primates, vision plays a critical role in learning. The underlying biological neural networks contain specialized neurons and synapses which not only sense and process visual stimuli but also learn and adapt with remarkable energy efficiency. Forgetting also plays an active role in learning. Mimicking the adaptive neurobiological mechanisms for seeing, learning, and forgetting can, therefore, accelerate the development of artificial intelligence (AI) and bridge the massive energy gap that exists between AI and biological intelligence. Here, we demonstrate a bioinspired machine vision system based on a 2D phototransistor array fabricated from large-area monolayer molybdenum disulfide (MoS2) and integrated with an analog, nonvolatile, and programmable memory gate-stack; this architecture not only enables dynamic learning and relearning from visual stimuli but also offers learning adaptability under noisy illumination conditions at miniscule energy expenditure. In short, our demonstrated "all-in-one" hardware vision platform combines "sensing", "computing", and "storage" to not only overcome the von Neumann bottleneck of conventional complementary metal-oxide-semiconductor (CMOS) technology but also to eliminate the need for peripheral circuits and sensors.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Redes Neurais de Computação
Idioma:
En
Revista:
ACS Nano
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos