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BRICMEM aims to enhance artificial intelligence hardware by developing brain-inspired computational memory. It focuses on using superposition, randomness, and temporal encoding to improve the efficiency and functionality of deep neural networks.
Deep neural networks (DNNs) have transformed the field of AI in recent years.
However, a significant challenge persists in the form of inefficient hardware implementations of DNNs.
Computation in memory (CIM) is an emerging approach that tackles the processor-memory divide in modern computing systems, enhancing their suitability for DNNs. CIM draws inspiration from certain computational principles found in the human brain, such as hard-wired neural networks and analogue processing.A key question…
RUPRECHT-KARLS-UNIVERSITAET HEIDELBERG
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