Large-scale AI models that enable next-generation applications like natural language processing and autonomous systems require intensive training and immense power. The monetary and environmental expense is too great.
This is where analog deep learning comes into play. The concept behind it is to develop a new type of hardware that can accelerate the training of neural networks, achieving a cheaper, more efficient, and more sustainable way to move forward with AI applications.
Murat Onen, a postdoctoral researcher in the Department of Electrical Engineering and Computer Science at MIT, explains.
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