Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

Convolutional Neural Networks (CNNs) are being increasingly used in safety-critical and
high-performance computing systems. As such systems require high levels of resilience to …

Energy consumption is one of the major issues in today's computer science, and an
increasing number of scientific communities are interested in evaluating the tradeoff …

We empirically evaluate an undervolting technique, ie, underscaling the circuit supply
voltage below the nominal level, to improve the power-efficiency of Convolutional Neural …

Convolutional Neural Networks (CNN) continue to dominate research in the area of
hardware acceleration using Field Programmable Gate Arrays (FPGA), proving its …

The most active research area for Field Programmable Gate Arrays is the Convolution
Neural Network (CNN), and the gist of any CNN is an activation function. Therefore, various …

Rapid growth in Deep Neural Network (DNN) workloads has increased the energy footprint
of the Artificial Intelligence (AI) computing realm. For optimum energy efficiency, we propose …

Convolutional neural networks (CNNs) are to be effective in many application domains,
especially in the computer vision area. In order to achieve lower latency CNN processing …

On-chip memory (usually based on Static RAMs—SRAMs) are crucial components for
various computing devices including heterogeneous devices, eg, GPUs, FPGAs, and ASICs …

Rapid growth in Deep Neural Network (DNN) workloads has increased the energy footprint
of the Artificial Intelligence (AI) computing realm. For optimum energy efficiency, we propose …