Publications associées à FLOPS

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

David Atienza Alonso, Giovanni Ansaloni, Grégoire Axel Eggermann, Marco Antonio Rios

State-of-the-art Artificial Intelligence (AI) algorithms, such as graph neural networks and recommendation systems, require floating-point computation of very large matrix multiplications over sparse data. Their execution in resource-constrained scenarios, ...

2023

Efficient Lung Nodule Detection via 3D Deep Learning with Shifted Convolutions

Jiancheng Yang, Bo Du, Kang Yuan

The high computational costs of deep convolutional neural networks hinder their deployment in real-world applications, including pulmonary nodule detection from CT scans where large 3D image sizes amplify the issue. This paper presents a novel 3D method to ...

IEEE2023

Neural Joint Space Implicit Signed Distance Functions for Reactive Robot Manipulator Control

Aude Billard, Mikhail Koptev, Nadia Barbara Figueroa Fernandez

In this paper, we present an approach for learning a neural implicit signed distance function expressed in joint space coordinates, that efficiently computes distance-to-collisions for arbitrary robotic manipulator configurations. Computing such distances ...

2022

High Performance Computing for gravitational lens modeling: Single vs double precision on GPUs and CPUs

Jean-Paul Richard Kneib, Markus Rexroth

Strong gravitational lensing is a powerful probe of cosmology and the dark matter distribution. Efficient lensing software is already a necessity to fully use its potential and the performance demands will only increase with the upcoming generation of tele ...

ELSEVIER2020

Smart Sensor Buoys - Scalable Solution for Continuous and Flexible Water Monitoring

Within this paper an industry-driven insight to an environmental development project will be given. The approach offers the novel possibility to interconnect commercially available sensors to economic and high-performance floating platforms. Each platform ...

Paul Scherrer Institute, World Resources Forum2019

Exploiting Flow Graph of System of ODEs to Accelerate the Simulation of Biologically-Detailed Neural Networks

Felix Schürmann, Michael Lee Hines, Bruno Ricardo Da Cunha Magalhães

Exposing parallelism in scientific applications has become a core requirement for efficiently running on modern distributed multicore SIMD compute architectures. The granularity of parallelism that can be attained is a key determinant for the achievable ac ...

IEEE2019

A machine learning approach for power gating the FPGA routing network

Mirjana Stojilovic, Hossein Asadi

Power gating is a common approach for reducing circuit static power consumption. In FPGAs, resources that dominate static power consumption lie in the routing network. Researchers have proposed several heuristics for clustering multiplexers in the routing ...

IEEE COMPUTER SOC2019

Review and Benchmarking of Precision-Scalable Multiply-Accumulate Unit Architectures for Embedded Neural-Network Processing

Christian Enz, Vincent Frédéric Camus

The current trend for deep learning has come with an enormous computational need for billions of Multiply-Accumulate (MAC) operations per inference. Fortunately, reduced precision has demonstrated large benefits with low impact on accuracy, paving the way ...

2019

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FLOPS

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

Reliable Microsecond-Scale Distributed Computing

Efficient Lung Nodule Detection via 3D Deep Learning with Shifted Convolutions

Neural Joint Space Implicit Signed Distance Functions for Reactive Robot Manipulator Control

High Performance Computing for gravitational lens modeling: Single vs double precision on GPUs and CPUs

Smart Sensor Buoys - Scalable Solution for Continuous and Flexible Water Monitoring

Exploiting Flow Graph of System of ODEs to Accelerate the Simulation of Biologically-Detailed Neural Networks

A machine learning approach for power gating the FPGA routing network

Review and Benchmarking of Precision-Scalable Multiply-Accumulate Unit Architectures for Embedded Neural-Network Processing

Design of approximate and precision-scalable circuits for embedded multimedia and neural-network processing

Graph Chatbot

Chattez avec Graph Search

Efficient Lung Nodule Detection via 3D Deep Learning with Shifted Convolutions

Reliable Microsecond-Scale Distributed Computing

Design of approximate and precision-scalable circuits for embedded multimedia and neural-network processing

A machine learning approach for power gating the FPGA routing network

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

Neural Joint Space Implicit Signed Distance Functions for Reactive Robot Manipulator Control

High Performance Computing for gravitational lens modeling: Single vs double precision on GPUs and CPUs

Smart Sensor Buoys - Scalable Solution for Continuous and Flexible Water Monitoring

Review and Benchmarking of Precision-Scalable Multiply-Accumulate Unit Architectures for Embedded Neural-Network Processing

Exploiting Flow Graph of System of ODEs to Accelerate the Simulation of Biologically-Detailed Neural Networks