The Continuous Learning (CL) paradigm consists of continuously evolving the parameters of
the Deep Neural Network (DNN) model to progressively learn to perform new tasks without …

Systolic arrays are the primary part of modern deep learning accelerators and are being
used widely in real-life applications such as self-driving cars. This paper presents a novel …

Recent advancements in optical convolutional neural networks (CNNs) and radar signal
processing systems have brought an increasing need for the adoption of optical fast Fourier …

Microcontroller Units (MCUs) are widely used for industrial field applications, and are now
ever more being used also for machine learning on the edge, because of their reliability, low …

In this paper we assess the performance of the new MATLAB Deep Learning Processor. It is
a hardware architecture meant for FPGA devices which is able to infer Convolutional Neural …

We focus on the evolutionary design of programs capable of capturing more randomness
and outliers in the input data set than the standard genetic programming (GP)-based …

Artificial intelligence in the Internet of Things (AIoT) is a promising technology for consumer
electronics. Battery life and package size are essential constraints for AI applications on …

Recent studies have shown that Machine Learning (ML) algorithm suffers from several
vulnerability threats. Among them, adversarial attacks represent one of the most critical …

We propose a distributed system based on low-power embedded FPGAs designed for edge
computing applications focused on exploring distributing scheduling optimizations for Deep …

Running machine learning algorithms (ML) on constrained devices at the extreme edge of
the network is problematic due to the computational overhead of ML algorithms, available …