14.4 All-Digital Time-Domain CNN Engine Using Bidirectional Memory Delay Lines for Energy-Efficient Edge Computing

Convolutional Neural Networks (CNN) provide superior classification accuracy in a variety of machine learning applications, such as image/speech/sensor data processing. However, CNNs require intensive compute and memory resources making it challenging to employ in energy-constrained edge-computing devices. Specifically, Multiply-and-Accumulate (MAC) operations consume a significant portion of the total CNN energy [1].