Due to recent advances in digital technologies, and availability of credible data, an area of
artificial intelligence, deep learning, has emerged and has demonstrated its ability and …

In the modern-day era of technology, a paradigm shift has been witnessed in the areas
involving applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep …

Approximate computing is claimed to be a powerful knob for alleviating the peak power and
energy-efficiency issues. However, providing a consistent benchmark suit with diverse …

Deep learning has been popularized by its recent successes on challenging artificial
intelligence problems. One of the reasons for its dominance is also an ongoing challenge …

It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …

Abstract Deep Neural Networks (DNNs) are compute-intensive learning models with
growing applicability in a wide range of domains. Due to their computational complexity …

Generative Adversarial Networks (GANs) are one of the most recent deep learning models
that generate synthetic data from limited genuine datasets. GANs are on the frontier as …

The challenging deployment of compute-intensive applications from domains such as
Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of …

Self-attention is a key enabler of state-of-art accuracy for various transformer-based Natural
Language Processing models. This attention mechanism calculates a correlation score for …

The trend of unsustainable power consumption and large memory bandwidth demands in
massively parallel multicore systems, with the advent of the big data era, has brought upon …