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An event camera, also known as a neuromorphic camera, [1] silicon retina, [2] or dynamic vision sensor, [3] is an imaging sensor that responds to local changes in brightness. Event cameras do not capture images using a shutter as conventional (frame) cameras do. Instead, each pixel inside an event camera operates independently and ...
[1] [2] A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. [3] [4] In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory ...
The system supports up to 1.15 billion neurons and 128 billion synapses distributed over 140,544 neuromorphic processing cores, consuming 2,600 watts of power. It includes over 2,300 embedded x86 processors for ancillary computations. Intel claimed in 2024 that Hala Point was the world’s largest neuromorphic system. It uses Loihi 2 chips.
Mead's contributions have arisen from the application of basic physics to the development of electronic devices, often in novel ways. During the 1960s, he carried out systematic investigations into the energy behavior of electrons in insulators and semiconductors, developing a deep understanding of electron tunneling, barrier behavior and hot electron transport. [13]
In general, Mott insulators occur when the repulsive Coulomb potential U is large enough to create an energy gap. One of the simplest theories of Mott insulators is the 1963 Hubbard model. The crossover from a metal to a Mott insulator as U is increased, can be predicted within the so-called dynamical mean field theory.
The Lytro ILLUM features a 40 megaray sensor (in comparison to the original Lytro Camera's 11 megaray sensor), and a more powerful processor. The 30-250mm lens has 8.3x optical zoom, an f/2.0 aperture, and 1:3 macro focus capability. The lens was designed to weigh half a pound to make the camera lighter and more agile.
Organic neuromorphic circuits made out of polymers, coated with an ion-rich gel to enable a material to carry an electric charge like real neurons, have been built into a robot, enabling it to learn sensorimotorically within the real world, rather than via simulations or virtually.
Spintronics emerged from discoveries in the 1980s concerning spin-dependent electron transport phenomena in solid-state devices. This includes the observation of spin-polarized electron injection from a ferromagnetic metal to a normal metal by Johnson and Silsbee (1985) [5] and the discovery of giant magnetoresistance independently by Albert Fert et al. [6] and Peter Grünberg et al. (1988). [7]