A5103425459- Advanced Memory and Neural Computing
- Analytical Chemistry and Sensors
- Advanced Sensor and Energy Harvesting Materials
- Neural Networks and Reservoir Computing
- Industrial Vision Systems and Defect Detection
- Neural dynamics and brain function
- Gas Sensing Nanomaterials and Sensors
Hebei University
2024-2025
Therapeutics Systems Research Laboratories (United States)
1995
Ripple (United States)
1995
In-sensor computing has emerged as an ultrafast and low-power technique for next-generation machine vision. However, in situ training of in-sensor systems remains challenging due to the demands both high-performance devices efficient programming schemes. Here, we experimentally demonstrate artificial neural network (ANN) based on ferroelectric photosensors (FE-PSs). Our FE-PS exhibits self-powered, fast (<30 μs), multilevel (>4 bits) photoresponses, well long retention (50 days), high...
Combining physics with computational models is increasingly recognized for enhancing the performance and energy efficiency in neural networks. Physical reservoir computing uses material dynamics of physical substrates temporal data processing. Despite ease training, building an efficient remains challenging. Here, we explore beyond conventional delay-based reservoirs by exploiting spatiotemporal transformation all-electric spintronic devices. Our nonvolatile effectively transforms history...
<title>Abstract</title> In-sensor computing has emerged as an ultrafast and low-power technique for next-generation machine vision. However, in situ training of in-sensor systems remains challenging due to the demands both high-performance devices efficient programming schemes. Here, we experimentally demonstrate artificial neural network (ANN) based on ferroelectric photosensors (FE-PSs). Our FE-PS exhibits self-powered, fast (<30 μs), multilevel (>4 bits) photoresponses, well long...