Dynamic vision sensor (DVS) cameras enable energy-activity proportional visual sensing by
only propagating events produced by changes in the observed scene. Furthermore, by …

Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart
Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data …

On-chip deep neural network (DNN) inference and training at the Extreme-Edge (TinyML)
impose strict latency, throughput, accuracy, and flexibility requirements. Heterogeneous …

Deep Neural Network (DNN) inference based on quantized narrow-precision integer data
represents a promising research direction toward efficient deep learning computations on …

Low-precision quantized neural networks (QNNs) reduce the required memory space,
bandwidth, and computational power, and hence are suitable for deployment in applications …

Computationally intensive algorithms such as Deep Neural Networks (DNNs) are becoming
killer applications for edge devices. Porting heavily data-parallel algorithms on resource …

The emerging trend of deploying complex algorithms, such as Deep Neural networks
(DNNs), increasingly poses strict memory and energy efficiency requirements on Internet-of …

The RISC-V architecture has recently emerged as a popular open source option for the
design of general purpose cores with a wide spectrum of operating specifications. In this …

In Deep Neural Networks (DNN), the depth-wise separable convolution has often replaced
the standard 2D convolution having much fewer parameters and operations. Another …

Extreme edge platforms, such as in-vehicle smart devices, require efficient deployment of
quantized deep neural networks (DNNs) to enable intelligent applications with limited …