Anomaly-based intrusion detection systems are designed to scan computer network traffic
for abnormal behavior. Binary classifiers based on supervised machine learning have …

Compiling sequential C programs for Connex-S, a competitive, scalable and customizable,
wide vector accelerator for intensive embedded applications with 32 to 4,096 16-bit integer …

Many neural architectures including RBF, SVM, FSVC classifiers, or deep-learning solutions
require the efficient implementation of neurons layers, each of them having a given number …

This paper discusses methods, algorithmic examples and general principles regarding
latency reduction methods for single chip Map-Reduce and Map-Scan many-core …

Gordon Moore's predictions refer, in addition to the resolution at which we can make the
devices, also to the size of the chips and to the" circuit cleverness". Considering Moore's law …

All-Path Shortest-Path Problem (APSP) algorithm implemented on two currently used
architectures, a mono-core one and on a many-core one, is compared with the …

Abstract Various forms of Deep Neural Network (DNN) architectures are used as Deep
Learning tools for neural inspired computational systems. The computational power, the …

The practice of parallel computing emphasizes frequently used patterns. Some of them can
be accelerated using appropriate networks of simple circuits. We propose a solution, based …

Spectral clustering is a Machine Learning technique intensively used in Big Data
applications. It makes extensive use of linear algebra. This article introduces the concept of …

We regard the MapReduce mechanism as a unifying principle in the domain of computer
science. Going back to the roots of AI and circuits, we show that the MapReduce mechanism …