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Cool, hyper dimensional
“Another advantage of hyperdimensional computing is transparency: The algebra clearly tells you why the system chose the answer it did. The same is not true for traditional neural networks. Olshausen, Rahimi, and others are developing hybrid systems in which neural networks map things in the physical world to hypervectors, and then hyperdimensional algebra takes over. “Things like analogical reasoning just fall in your lap,” Olshausen said. “This is what we should expect of any AI system. We should be able to understand it just like we understand an airplane or a television set.”
All of these benefits over traditional computing suggest that hyperdimensional computing is well suited for a new generation of extremely sturdy, low-power hardware. It’s also compatible with “in-memory computing systems,” which perform the computing on the same hardware that stores data (unlike existing von Neumann computers that inefficiently shuttle data between memory and the central processing unit). Some of these new devices can be analog, operating at very low voltages, making them energy-efficient but also prone to random noise. For von Neumann computing, this randomness is “the wall that you can’t go beyond,” Olshausen said. But with hyperdimensional computing, “you can just punch through it.”
Despite such advantages, hyperdimensional computing is still in its infancy. “There’s real potential here,” Fermüller said. But she points out that it still needs to be tested against real-world problems and at bigger scales, closer to the size of modern neural networks.”
Source: https://www.quantamagazine.org/a-ne...-reimagines-artificial-intelligence-20230413/
“Another advantage of hyperdimensional computing is transparency: The algebra clearly tells you why the system chose the answer it did. The same is not true for traditional neural networks. Olshausen, Rahimi, and others are developing hybrid systems in which neural networks map things in the physical world to hypervectors, and then hyperdimensional algebra takes over. “Things like analogical reasoning just fall in your lap,” Olshausen said. “This is what we should expect of any AI system. We should be able to understand it just like we understand an airplane or a television set.”
All of these benefits over traditional computing suggest that hyperdimensional computing is well suited for a new generation of extremely sturdy, low-power hardware. It’s also compatible with “in-memory computing systems,” which perform the computing on the same hardware that stores data (unlike existing von Neumann computers that inefficiently shuttle data between memory and the central processing unit). Some of these new devices can be analog, operating at very low voltages, making them energy-efficient but also prone to random noise. For von Neumann computing, this randomness is “the wall that you can’t go beyond,” Olshausen said. But with hyperdimensional computing, “you can just punch through it.”
Despite such advantages, hyperdimensional computing is still in its infancy. “There’s real potential here,” Fermüller said. But she points out that it still needs to be tested against real-world problems and at bigger scales, closer to the size of modern neural networks.”
Source: https://www.quantamagazine.org/a-ne...-reimagines-artificial-intelligence-20230413/