In 1982, Kwabena Boahen had his first computer, when he was only a teenager who lived in Accra, Ghana. "That is really a very cool device," he recalls. But when Bo Heng figured out how the internal workings of the computer, he didn't feel particularly shocked. "I figured out how the central processor constantly transfers data back and forth. I thought, 'Scorpio! The computer needs to work madly all the time'." He instinctively felt that the computer needed more "African" features in the design. The design: the distribution is more extensive, more fluid, less strict.
Today, as a bioengineer at Stanford University in the United States, Bo Heng and other researchers have formed a small team that is trying to create the ideal computer operating model by mimicking the brain.
The energy savings of the brain are significant, and its computing power is enough to challenge the world's largest supercomputer, although it relies on components that are not perfect: neurons are very slow, variable, and confusing. In a smaller area than the shoebox, the brain can complete the understanding of language, abstract reasoning, control motion and even more tasks, but the power consumption is smaller than the household light bulb, and there is no tiny processor similar to the central processor.
To make silicon chips work like the brain, researchers are building non-digital chip systems that work as much as possible with real neural networks. A few years ago, Bochen designed a device called the Neurogrid, which was used to simulate the activity of 1 million neurons. The number of simulations was almost as many as the neurons in the bee's brain. To this day, "neuromorphic technology" has evolved over a quarter of a century, and it is not far from practical applications. This technology is expected to be used on any low-power and small-volume device, from smartphones to robots to artificial eyes and ears. In the past five years, such application prospects have attracted many researchers to participate in this field, and institutions in the United States and Europe have invested hundreds of millions of dollars in research funding.
Giacomo Indiveri of the Institute of Neuroinformatics (INI) believes that neuromorphic devices also provide powerful research tools for neuroscientists. In a real physical system, by observing which functions a neural model has or is missing, "scientists can understand why the brain structure is like this."
Bohen said that neuromorphic solutions should help break through the limitations of Moore's Law. For a long time, every two years or so, computer chip manufacturers need to double the number of transistors in a given space. The use of chip space has tended to the extreme. Soon, the circuit on the silicon chip will be too small and too tight to transmit a "pure" signal: electrons will "leak" from various components, causing silicon chips and The neurons are just as chaotic. Some researchers hope to solve this problem by using software patches, such as borrowing techniques like statistical error correction to make the Internet work smoothly. But in the end, Bohen said that the most effective solution still exists in our brains - something that has been around for millions of years.
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