In today's world, the applications of convolutional neural networks (CNN) are limitless and
are employed in numerous fields. The CNNs get wider and deeper to achieve near-human …

Weightless neural networks (WNNs) are a class of machine learning model which use table
lookups to perform inference, rather than the multiply-accumulate operations typical of deep …

Weightless neural networks (WNNs) are an alternative pattern recognition technique where
RAM nodes function as neurons. As both training and inference require mostly table …

In this paper, we implemented a high accuracy, high throughput, low bit-width, and low
power consumption convolutional neural network (CNN) inference system for handwritten …

Transfer learning has proven to be a valuable technique for deploying machine learning
models on edge devices and embedded systems. By leveraging pre-trained models and fine …

Bindings for machine learning frameworks (such as TensorFlow and PyTorch) allow
developers to integrate a framework's functionality using a programming language different …

Traffic Classification (TC) systems are designed to identify the applications generating
network traffic. Recent advancements in TC leverage Deep Learning (DL) techniques …

Due to the great increase of the on-chip block memory for the latest field programmable gate
array (FPGA), the highly efficient utilization of the on-chip DSP Slice has become the …

Wireless capsule endoscopy (WCE) captures huge number of images, but only a fraction are
medically relevant. We propose automated real‐time small bowel visualization quality …

Thanks to the great developments of the latest Field Programmable Gate Array (FPGA), the
performance bottleneck of Deep Learning hardware accelerators has been converted to …