Improving the efficiency of AI accelerators and image processing in College of Engineering
Researchers at the School of Electrical and Computer Engineering at the Faculty of Engineering have succeeded in significantly reducing power consumption, chip area, and computing latency by presenting a new architecture for floating-point multipliers; an achievement that can play an important role in increasing the efficiency of artificial intelligence systems, neural networks, and image processing
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