Publications related to Single instruction, multiple data

Performance Estimation of High-Level Dataflow Program on Heterogeneous Platforms by Dynamic Network Execution

Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch

The performance of programs executed on heterogeneous parallel platforms largely depends on the design choices regarding how to partition the processing on the various different processing units. In other words, it depends on the assumptions and parameters ...

MDPI2022

Efficient Massively Parallel Join Optimization for Large Queries

Anastasia Ailamaki, Bikash Chandra, Srinivas Karthik Venkatesh, Riccardo Mancini, Vasileios Mageirakos

Modern data analytical workloads often need to run queries over a large number of tables. An optimal query plan for such queries is crucial for being able to run these queries within acceptable time bounds. However, with queries involving many tables, find ...

2022

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat, Apostolos Pyrgelis, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Sinem Sav

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

INTERNET SOC2021

Blade: An in-Cache Computing Architecture for Edge Devices

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Marco Antonio Rios, William Andrew Simon, Yasir Mahmood Qureshi

Area and power constrained edge devices are increasingly utilized to perform compute intensive workloads, necessitating increasingly area and power efficient accelerators. In this context, in-SRAM computing performs hundreds of parallel operations on spati ...

2020

Biosensors for Bimolecular Computing: a Review and Future Perspectives

Sandro Carrara, Danilo Demarchi, Simone Aiassa

Biomolecular computing is the field of engineering where computation, storage, communication, and coding are obtained by exploiting interactions between biomolecules, especially DNA, RNA, and enzymes. They are a promising solution in a long-term vision, br ...

SPRINGER2020

Y QC-MDPC Decoders with Several Shades of Gray

Dusan Kostic

QC-MDPC code-based KEMs rely on decoders that have a small or even negligible Decoding Failure Rate (DFR). These decoders should be efficient and implementable in constant-time. One example for a QC-MDPC KEM is the Round-2 candidate of the NIST PQC standar ...

SPRINGER INTERNATIONAL PUBLISHING AG2020

An Optimizing Multi-platform Source-to-source Compiler Framework for the NEURON MODeling Language

Felix Schürmann, James Gonzalo King, Michael Lee Hines, Pramod Shivaji Kumbhar, Jorge Blanco Alonso, Omar Awile, Liam Roger George Keegan

Domain-specific languages (DSLs) play an increasingly important role in the generation of high performing software. They allow the user to exploit domain knowledge for the generation of more efficient code on target architectures. Here, we describe a new c ...

Springer2020

Side-channel attacks on SIKE

Natacha Yolande Emmanuel Marie Linard de Guertechin

This thesis presents, firstly, an introduction to the current state of the art in isogeny-based cryptography, and secondly, a side-channel differential power analysis of SIKE—an isogeny-based key exchange algorithm—in semi-static mode. These attacks have b ...

2020

BLADE: A BitLine Accelerator for Devices on the Edge

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, William Andrew Simon, Yasir Mahmood Qureshi

The increasing ubiquity of edge devices in the consumer market, along with their ever more computationally expensive workloads, necessitate corresponding increases in computing power to support such workloads. In-memory computing is attractive in edge devi ...

2019

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Single instruction, multiple data

Performance Estimation of High-Level Dataflow Program on Heterogeneous Platforms by Dynamic Network Execution

Efficient Massively Parallel Join Optimization for Large Queries

Micro-architectural Analysis of Database Workloads

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Blade: An in-Cache Computing Architecture for Edge Devices

Biosensors for Bimolecular Computing: a Review and Future Perspectives

Y QC-MDPC Decoders with Several Shades of Gray

An Optimizing Multi-platform Source-to-source Compiler Framework for the NEURON MODeling Language

Side-channel attacks on SIKE

BLADE: A BitLine Accelerator for Devices on the Edge

Graph Chatbot

Chat with Graph Search

Micro-architectural Analysis of Database Workloads

An Optimizing Multi-platform Source-to-source Compiler Framework for the NEURON MODeling Language

Biosensors for Bimolecular Computing: a Review and Future Perspectives

Efficient Massively Parallel Join Optimization for Large Queries

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Blade: An in-Cache Computing Architecture for Edge Devices

Side-channel attacks on SIKE

BLADE: A BitLine Accelerator for Devices on the Edge

Performance Estimation of High-Level Dataflow Program on Heterogeneous Platforms by Dynamic Network Execution

Y QC-MDPC Decoders with Several Shades of Gray