Publications associées à Half-precision floating-point format

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Aleksandra Radenovic, Andras Kis, Mukesh Kumar Tripathi, Zhenyu Wang, Guilherme Migliato Marega

Machine learning and signal processing on the edge are poised to influence our everyday lives with devices that will learn and infer from data generated by smart sensors and other devices for the Internet of Things. The next leap toward ubiquitous electron ...

2022

ColTraIn: Co-located DNN training and inference

Mario Paulo Drumond Lages De Oliveira

Deep neural network inference accelerators are deployed at scale to accommodate online services, but face low average load because of service demand variability, leading to poor resource utilization. Unfortunately, reclaiming inference idle cycles is diffi ...

EPFL2020

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

Mixed-Precision Deep Learning Based on Computational Memory

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has ...

FRONTIERS MEDIA SA2020

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits their deployment in energy and memory limited applications that require rea ...

IEEE2020

Lactate measurement by neurochemical profiling in the dorsolateral prefrontal cortex at 7T: accuracy, precision, and relaxation times

Pierre Magistretti, Lijing Xin, Kim Do, Masoumeh Dehghani Moghadam

Purpose This assesses the potential of measuring lactate in the human brain using three non-editing MRS methods at 7T and compares the accuracy and precision of the methods. Methods H-1 MRS data were measured in the right dorsolateral prefrontal cortex usi ...

2020

Self-Binarizing Networks

Sabine Süsstrunk, Radhakrishna Achanta, Fayez Lahoud, Pablo Marquez Neila

We present a method to train self-binarizing neural networks, that is, networks that evolve their weights and activations during training to become binary. To obtain similar binary networks, existing methods rely on the sign activation function. This funct ...

arXiv2019

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

Survey of Precision-Scalable Multiply-Accumulate Units for Neural-Network Processing

Christian Enz, Vincent Frédéric Camus

IEEE2019

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Half-precision floating-point format

Towards General-Purpose Decentralized Computing with Permissionless Extensibility

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

ColTraIn: Co-located DNN training and inference

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

Mixed-Precision Deep Learning Based on Computational Memory

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning

Lactate measurement by neurochemical profiling in the dorsolateral prefrontal cortex at 7T: accuracy, precision, and relaxation times

Self-Binarizing Networks

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

Survey of Precision-Scalable Multiply-Accumulate Units for Neural-Network Processing

Graph Chatbot

Chattez avec Graph Search

Towards General-Purpose Decentralized Computing with Permissionless Extensibility

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Self-Binarizing Networks

ColTraIn: Co-located DNN training and inference

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning

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

Mixed-Precision Deep Learning Based on Computational Memory

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

Lactate measurement by neurochemical profiling in the dorsolateral prefrontal cortex at 7T: accuracy, precision, and relaxation times

Survey of Precision-Scalable Multiply-Accumulate Units for Neural-Network Processing