But the computation these algorithms do can be simplified to one specific operation, called the dot product.Our approach was, can we implement this dot-product functionality inside the memory so that you don't need to transfer this data back and forth?""One of the keys to the system is that all the weights are either 1 or -1.That means that they can be implemented within the memory itself as simple switches that either close a circuit or leave it open.""In experiments, they ran the full implementation of a neural network on a conventional computer and the binary-weight equivalent on their chip.When you usually apply foundation under eyes, you end up putting on more than required which looks so odd afterwards.Follow "A special-purpose chip that increases the speed of neural-network computations by three to seven times over its predecessors, while reducing power consumption 94 to 95 percent.""Since these machine-learning algorithms need so many computations, this transferring back and forth of data is the dominant portion of the energy consumption.
This sounds reasonable at first, but host galaxies are 10 billion times bigger than the central black holes; it should be difficult for two objects of such vastly different scales to directly affect each other. Aiming to solve this shadowy problem, a team of astronomers utilized the high resolution of ALMA to observe the centre of spiral galaxy M77.
The central region of M77 is an “active galactic nucleus,” or AGN, which means that matter is vigorously falling toward the central supermassive black hole and emitting intense light.
AGNs can strongly affect the surrounding environment, therefore they are important objects for solving the mystery of the co-evolution of galaxies and black holes.
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