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Chapter and Conference Paper
Speeding Up Non-archimedean Numerical Computations Using AVX-512 SIMD Instructions
This work presents the acceleration of a Bounded Algorithmic Number (BAN) library exploiting vector instructions in general-purpose processors. With the use of this encoding, it is possible to represent non-Ar...
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Chapter and Conference Paper
Towards Formal Verification of Neural Networks in Cyber-Physical Systems
Machine Learning approaches have been successfully used for the creation of high-performance control components of cyber-physical systems, where the control dynamics result from the combination of many subsyst...
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Article
Open AccessPure and mixed lexicographic-paretian many-objective optimization: state of the art
This work aims at reviewing the state of the art of the field of lexicographic multi/many-objective optimization. The discussion starts with a review of the literature, emphasizing the numerous application in ...
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Chapter and Conference Paper
Design and FPGA Synthesis of BAN Processing Unit for Non-Archimedean Number Crunching
This work presents the design and synthesis of a processing unit for numbers encoded according to the recently introduced BAN format. Such an encoding allows one to represent numbers which are not only finite ...
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Chapter and Conference Paper
Experiments on Speeding Up the Recursive Fast Fourier Transform by Using AVX-512 SIMD Instructions
The Fast Fourier Transform is probably one of the most studied algorithms of all time. New techniques regarding hardware and software are often applied and tested on it, but the interest in FFT is still large bec...
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Chapter and Conference Paper
Decoding-Free Two-Input Arithmetic for Low-Precision Real Numbers
In this work, we present a novel method for directly computing functions of two real numbers using logic circuits without decoding; the real numbers are mapped to a particularly-chosen set of integer numbers. ...
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Article
Open AccessPhotonic-aware neural networks
Photonics-based neural networks promise to outperform electronic counterparts, accelerating neural network computations while reducing power consumption and footprint. However, these solutions suffer from phys...
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Chapter and Conference Paper
Experimental Results of Vectorized Posit-Based DNNs on a Real ARM SVE High Performance Computing Machine
With the pervasiveness of deep neural networks in scenarios that bring real-time requirements, there is the increasing need for optimized arithmetic on high performance architectures. In this paper we adopt tw...
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Chapter
On the Use of Grossone Methodology for Handling Priorities in Multi-objective Evolutionary Optimization
This chapter introduces a new class of optimization problems, called Mixed Pareto-Lexicographic Multi-objective Optimization Problems (MPL-MOPs), to provide a suitable model for scenarios where some objectives...
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Chapter
Computing Optimal Decision Strategies Using the Infinity Computer: The Case of Non-Archimedean Zero-Sum Games
As is well known, zero-sum games are appropriate instruments for the analysis of several issues across areas including economics, international relations and engineering, among others. In particular, the Nash ...
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Chapter and Conference Paper
Small Reals Representations for Deep Learning at the Edge: A Comparison
The pervasiveness of deep neural networks (DNNs) in edge devices enforces new requirements on information representation. Low precision formats from 16 bits down to 1 or 2 bits have been proposed in the last y...
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Chapter
Multi-objective Lexicographic Mixed-Integer Linear Programming: An Infinity Computer Approach
In this chapter we show how a lexicographic multi-objective linear programming problem (LMOLP) can be transformed into an equivalent, single-objective one, by using the Grossone Methodology. Then we provide a ...
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Article
Open AccessThe Big-M method with the numerical infinite M
Linear programming is a very well known and deeply applied field of optimization theory. One of its most famous and used algorithms is the so called Simplex algorithm, independently proposed by Kantorovič and ...
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Article
Open AccessVectorizing posit operations on RISC-V for faster deep neural networks: experiments and comparison with ARM SVE
With the arrival of the open-source RISC-V processor architecture, there is the chance to rethink Deep Neural Networks (DNNs) and information representation and processing. In this work, we will exploit the fo...
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Chapter and Conference Paper
Handling Priority Levels in Mixed Pareto-Lexicographic Many-Objective Optimization Problems
This paper studies a class of mixed Pareto-Lexicographic multi-objective optimization problems where the preference among the objectives is available in different priority levels (PLs) before the start of the opt...
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Article
Fast deep neural networks for image processing using posits and ARM scalable vector extension
With the advent of image processing and computer vision for automotive under real-time constraints, the need for fast and architecture-optimized arithmetic operations is crucial. Alternative and efficient repr...
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Chapter and Conference Paper
Grossone Methodology for Lexicographic Mixed-Integer Linear Programming Problems
In this work we have addressed lexicographic multi-objective linear programming problems where some of the variables are constrained to be integer. We have called this class of problems LMILP, which stands for...
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Chapter and Conference Paper
Generalizing Pure and Impure Iterated Prisoner’s Dilemmas to the Case of Infinite and Infinitesimal Quantities
In this work, a generalization of both Pure and Impure iterated Prisoner’s Dilemmas is presented. More precisely, the generalization concerns the use of non-Archimedean quantities, i.e., payoffs that can be in...
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Chapter and Conference Paper
A Fast Approximation of the Hyperbolic Tangent When Using Posit Numbers and Its Application to Deep Neural Networks
Deep Neural Networks (DNNs) are being used in more and more fields. Among the others, automotive is a field where deep neural networks are being exploited the most. An important aspect to be considered is the ...
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Chapter
mspMEA: The Microcones Separation Parallel Multiobjective Evolutionary Algorithm and Its Application to Fuzzy Rule-Based Ship Classification
This chapter presents a new parallel multiobjective evolutionary algorithm, based on the island model, where the objective space is exploited to distribute the individuals among the processors. The algorithm, ...
