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A Construction Kit for Efficient Low Power Neural Network Accelerator Designs
This work provides a survey of neural network accelerator optimization approaches that have been used in recent works and reports their individual effects on edge processing performance.
NN2CAM: Automated Neural Network Mapping for Multi-Precision Edge Processing on FPGA-Based Cameras
The record-breaking achievements of deep neural networks (DNNs) in image classification and detection tasks resulted in a surge of new computer vision applications during the past years.
Improving Memory Utilization in Convolutional Neural Network Accelerators
While the accuracy of convolutional neural networks has achieved vast improvements by introducing larger and deeper network architectures, also the memory footprint for storing their parameters and activations has increased.
