Internet of Things (IoT) can help to pave the way to the circular economy and to a more
sustainable world by enabling the digitalization of many operations and processes, such as …

Human Activity Recognition (HAR) based on inertial data is an increasingly diffused task on
embedded devices, from smartphones to ultra low-power sensors. Due to the high …

Event-based sensors are drawing increasing attention due to their high temporal resolution,
low power consumption, and low bandwidth. To efficiently extract semantically meaningful …

Human Activity Recognition (HAR) has become an increasingly popular task for embedded
devices such as smartwatches. Most HAR systems for ultra-low power devices are based on …

Binarized convolutional neural network (BCNN) is a promising and efficient technique
toward the landscape of Artificial Intelligence of Things (AIoT) applications. In-Memory …

We present a 3.1 POp/s/W fully digital hardware accelerator for ternary neural networks
(TNNs). CUTIE, the completely unrolled ternary inference engine, focuses on minimizing …

The deployment of neural networks on edge devices has created a growing need for energy-
efficient computing. In this paper, we propose an all-digital standard cell-based time-domain …

This paper investigates a runtime accuracy reconfigurable implementation of an energy
efficient deep learning accelerator. It is based on voltage overscaling (VOS) technique which …

The rapid proliferation of the Internet of Things and the dramatic resurgence of artificial
intelligence based application workloads have led to immense interest in performing …

Abstract The Tsetlin Machine (TM) is a machine learning algorithm based on an ensemble of
Tsetlin Automata (TAs) that learns propositional logic expressions from Boolean input …