A device brings memory and processing together, helping minimizing errors and avoiding increasing energy demands due to huge amounts of data.
![Eliminating computer errors by combining computation and memory](https://www.advancedsciencenews.com/wp-content/uploads/2023/02/angeles-perez-RBiD978JXPM-unsplash.jpg)
A device brings memory and processing together, helping minimizing errors and avoiding increasing energy demands due to huge amounts of data.
The power demands of the Internet of Things could be combated with computing systems that mimic biological neurons.
Borrowing its shape from a disposable to-go cup lid, this new drone wing adapts to its surroundings all on its own.
Memristor-based sensing devices generate biological-like electrical signals that mimic those found in the brain for better computing.
Using atomically-thin 2D films, researchers have developed a nano-scale random number generator with enhanced long-term stability and reduced power consumption.
Researchers demonstrate the controlled growth of artificial synapses, paving the way for computers that can grow, evolve, and adapt like the human brain.
Bursting dynamics that mimic the functions of the human brain pave the way for more efficient AI systems.
Controlling the probability of a series of seemingly random events is the key to mimicking the human brain to optimize neuromorphic learning.
Resistive switching occurs when a dielectric suddenly changes its resistance in the presence of a strong electric field. This phenomenon underpins the behavior of devices such as memristors and neuromorphic memories. In Advanced Materials, Prof. Manfred Martin of...
An artificial neural network based entirely on memristors is developed.