Wearable devices are a fast-growing technology with impact on personal healthcare for both
society and economy. Due to the widespread of sensors in pervasive and distributed …

Machine learning has emerged as the dominant tool for implementing complex cognitive
tasks that require supervised, unsupervised, and reinforcement learning. While the resulting …

Many applications require sparse neural networks due to space or inference time
restrictions. There is a large body of work on training dense networks to yield sparse …

Vision transformers (ViTs) have recently received explosive popularity, but their enormous
model sizes and training costs remain daunting. Conventional post-training pruning often …

In this paper, we propose an elegant solution that is directly addressing the bottlenecks of
the traditional deep learning approaches and offers an explainable internal architecture that …

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …

The von Neumann computing architecture consists of separated processing and memory
elements; it is too bulky and energy-intensive to be implemented in the upcoming artificial …

SpiNNaker is an ARM-based processor platform optimized for the simulation of spiking
neural networks. This brief describes the roadmap in going from the current SPINNaker1 …

Spiking neural networks (SNNs) have manifested remarkable advantages in power
consumption and event-driven property during the inference process. To take full advantage …

Recent trends in deep learning (DL) imposed hardware accelerators as the most viable
solution for several classes of high-performance computing (HPC) applications such as …