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Analog Deep Learning Assures Faster Computation At Lesser Energy

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Analog Deep Learning Assures Faster Computation At Lesser Energy

Analog deep learning is faster and more energy-efficient than its digital counterpart

Analog deep learning, a new branch of artificial intelligence, promises faster computation with less energy consumption. Analog deep learning is faster and more energy-efficient than its digital counterpart. The development of AI applications and the computing platforms for them may be overlooking an alternative technology. Engineers working on analog deep learning have found a way to propel protons through solids at unprecedented speeds with the amount of time, effort, and money needed to train ever-more-complex neural network models are soaring as researchers push the limits of machine learning. Programmable resistors are the key building blocks in analog deep learning.

Analog deep learning promises faster computation with less energy consumption:

MIT researchers utilized a practical inorganic material in the fabrication process.  The new material is compatible with silicon fabrication techniques and the very powerful nanofabrication techniques we have at MIT.nano. This work has put these devices at a point where they now look promising for future applications. This could help scientists develop deep learning models much more quickly.

The key element of MIT’s new analog processor technology is known as a protonic programmable resistor, this new processor, increases and decreases the electrical conductance of protonic resistors enabling analog machine learning. To develop a super-fast and highly energy-efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte.  It lays the foundation for a new class of memory devices for powering deep learning algorithms.

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They have demonstrated the effectiveness of these programmable resistors, the researchers plan to re-engineer them for high-volume manufacturing. This work demonstrates that proton-based memory devices deliver impressive and surprising switching speed and endurance. Researchers are going to be essential to innovate in the future. The path forward is still going to be very challenging, but at the same time, it is very exciting.

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