Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Neural networks (NNs) are effective machine learning models that require significant
hardware and energy consumption in their computing process. To implement NNs …

The breakthrough of deep learning has started a technological revolution in various areas
such as object identification, image/video recognition and semantic segmentation. Neural …

Arithmetic plays a major role in a computer? s performance and efficiency. Building new
computing platforms supported by the traditional binary arithmetic and silicon-based …

Unary computing, whose arithmetics require only one logic gate, has enabled efficient DNN
processing, especially on strictly power-constrained devices. However, unary computing still …

Images are often corrupted with noise. As a result, noise reduction is an important task in
image processing. Common noise reduction techniques, such as mean or median filtering …

Stochastic computing (SC) is an old but reviving computing paradigm for its simple data path
that can perform various arithmetic operations. It allows for low power implementation, which …

The appealing property of low area, low power, and high bit error tolerance has made
Stochastic Computing (SC) a promising alternative to conventional binary arithmetic for …

Stochastic computing (SC) has a substantial amount of study on application-specific
integrated circuit (ASIC) design for artificial intelligence (AI) edge computing, especially the …

Neurons are the primary building block of the nervous system. Exploring the mysteries of the
brain in science or building a novel brain-inspired hardware substrate in engineering are …