Abstract:Convolutional neural networks (CNNs) play a key role in deep learning applications. However, the high computational complexity and high-energy consumption of CNNs trammel their application in hardware accelerators. Computing-in-memory (CIM) is the technique of running calculations entirely in memory (in our design, we use SRAM). CIM architecture has demonstrated great potential to effectively compute large-scale matrix-vector multiplication. CIM-based architecture for event detection is designed to trigger the… Show more
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