NEURAL HARDWARE
Unlike conventional computers, with separate memory and processing, neural networks serve as both memory and processor. And since processing can occur in localized sections throughout, brains are also much better at multitasking. Traditional computers have to do most of that multitasking in a sequence, so it takes ages. Spaun would do much better running on hardware that works more like a neural network does.
The image above represent a memristor array.At each crossing point in the grid, a memristor modulates the connection between the wires. In a neural network chip, electronic neurons sit at the edge of the grid and communicate through the wires and connections, with the memristors regulating the signals like biological synapses.
This hardware isn’t ready to scale to brainy proportions, in part because neuroscience doesn’t yet offer a clear picture of how to structure a brain. But it’s not too soon for the arrays to begin proving themselves capable of behaving like neural networks. Lu and his team have recently shown that crossbar memristor arrays can do the work of a virtual neural network, breaking down images into their features. This is a first step toward image recognition in a memristor network.
“Biological systems are very efficient dealing with very complex tasks in very complex environments because our hardware is built very differently,” said Lu.
Many consider artificial neural networks implemented on chips to be the next leap in computing, allowing faster processing with lower power needs for tasks like handling images, audio and video. Hardware is facing bottlenecks because we can’t keep making devices faster and faster. This has forced people to reexamine neuromorphic approaches.
Qualcomm and IBM are right on this with the new chips Zeroth and True North. These chips are hardwired with versions of neurons and synapses in traditional computing parts, but a relatively new electronic component enables a more direct parallel with biology.
Memristors, only around since 2008, can play the role of the synapse. Like a synapse, a memristor modulates how easily an electrical current can pass depending on the current that came before. The memristor naturally allows the current flow between the wires to vary on a continuum. In contrast, the transistors in traditional processors either allow current to pass or they don’t. The different levels of resistance enable memristors to store more information in each connection.
The neuromorphic networks made by Lu’s group don’t look much like the wild web of cells in a biological neural network. Instead, the team produces orderly layers of parallel wires, with each layer running perpendicular to its neighbors. Memristors sit at the crossing points, linking the wires. The neurons are at the edges of the grid, communicating with one another through the wires and memristors. The circuits can be designed so that, like their biological counterparts, the electronic neurons only send out an electrical pulse after reaching a certain level of current input.
The image above represent a memristor array.At each crossing point in the grid, a memristor modulates the connection between the wires. In a neural network chip, electronic neurons sit at the edge of the grid and communicate through the wires and connections, with the memristors regulating the signals like biological synapses.
This hardware isn’t ready to scale to brainy proportions, in part because neuroscience doesn’t yet offer a clear picture of how to structure a brain. But it’s not too soon for the arrays to begin proving themselves capable of behaving like neural networks. Lu and his team have recently shown that crossbar memristor arrays can do the work of a virtual neural network, breaking down images into their features. This is a first step toward image recognition in a memristor network.
“Biological systems are very efficient dealing with very complex tasks in very complex environments because our hardware is built very differently,” said Lu.
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