… CONCLUSION This paper presents seven distinct deep learning IoT applications. On these
… also achieving ultra low energy consumption utilizing a FC-NN accelerator. The energy con…

… Convolutional Neural Networks (CNN) algorithms have been achieving outstanding
classification capabilities in several complex tasks such as image recognition [2], [3], face …

… ABSTRACT Convolutional neural networks (CNNs) based deep learning algorithms require
high data flow and computational intensity. For real-time industrial applications, they need to …

… We introduce an ultra-low-power CNN FR processor and a … For ultra-low power consumption,
it adopts 3 key features: 1) … for low-power face detection (FD), 2) an ultra-low-power CNNP …

… This paper proposes an ultra-low power speech recognition processor … with energy efficiency
of 2.46 pJ/Neuron and power consumption of 141 μW. Binary convolutional neural network (…

… Ultra low power DNN can be realized by dedicated hardware implementations. When … of
deep learning architectures, there are several approaches that utilize multiple neural networks …

… Therefore, we propose an ultra-low-power (… power by 26.9×. Second, to understand the
scene, we enable hierarchical event recognition through a programmable deep neural network (…

… abilities with Spiking Neural Networks (SNNs). SNNs also enable us to harness the
power efficiency of neuromorphic engineering to build ultra-low power neural network systems. …

… efficient mapping of a Convolutional Neural Network task graph to the underlying Orlando
computing and memory resources required by the execution of convolutional layers by slicing …

… power while [42] has a too big area compared to its computation ability. However, if the
targeting application requires ultra-low power consumption … ability and power consumption can …