Neuromorphic photonics/electronics is the future of ultralow energy intelligent computing and artificial intelligence (AI). In recent years, inspired by the human brain, artificial neuromorphic devices have attracted extensive ...
Recent advances in pre-programmed architected materials could enable new functions that can evolve in response to their environments or external stimuli, according to Lawrence Livermore National Laboratory (LLNL) researchers.
Ferroelectric materials have found widespread use in everyday technology mainly owing to their electric polarization that can be switched between two distinct states. Overcoming the binary limit of ferroelectrics in order ...
Imagine a computer that can think as fast as the human brain while using very little energy. That's the goal of scientists seeking to discover or develop materials that can send and process signals as easily as the brain's ...
Compact, CMOS compatible on-chip photon sources have attracted much attention to the scientific community and the semiconductor industry. As the transistor's feature size is continuously scaling down, the integration density ...
Chaos isn't always harmful to technology, in fact, it can have several useful applications if it can be detected and identified.
Currently, AI services spread rapidly in daily life and in all industries. These services are enabled by connecting AI centers and terminals, such as mobile devices, PCs, etc. This method, however, increases the burden on ...
Extremely small arrays of magnets with strange and unusual properties can order themselves by increasing entropy, or the tendency of physical systems to disorder, a behavior that appears to contradict standard thermodynamics—but ...
In recent years, artificial intelligence has become ubiquitous, with applications such as speech interpretation, image recognition, medical diagnosis, and many more. At the same time, quantum technology has been proven capable ...
With the advent of Big Data, current computational architectures are proving to be insufficient. Difficulties in decreasing transistors' size, large power consumption and limited operating speeds make neuromorphic computing ...
