Publications associées à Double-precision floating-point format

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

Multi-ReRAM synapses for artificial neural network training

Elmira Shahrabi, Igor Krawczuk, Yusuf Leblebici, Irem Boybat Kara, Carlo Ricciardi, Cecilia Giovinazzo, Evangelos Eleftheriou, Abu Sebastian

Metal-oxide-based resistive memory devices (ReRAM) are being actively researched as synaptic elements of neuromorphic co-processors for training deep neural networks (DNNs). However, device-level non-idealities are posing significant challenges. In this wo ...

IEEE2019

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

AdaptHD: Adaptive Efficient Training for Brain-Inspired Hyperdimensional Computing

Giovanni De Micheli, Samuel Bosch, Mohsen Imani

Brain-inspired Hyperdimensional (HD) computing is a promising solution for energy-efficient classification. However, the existing HD computing algorithms have a lack of controllability on the training iterations which often results in slow training or dive ...

2019

Trading off Energy versus Accuracy in Modern Computing Systems

Jérémy Lucien Maurice Schlachter

The slowdown of Moore's law, which has been the driving force of the electronics industry over the last 5 decades, is causing serious problem to Integrated Circuits (ICs) improvements. Technology scaling is becoming more and more complex and fabrication co ...

EPFL2018

Training DNNs with Hybrid Block Floating Point

Babak Falsafi, Martin Jaggi, Tao Lin, Mario Paulo Drumond Lages De Oliveira

The wide adoption of DNNs has given birth to unrelenting computing requirements, forcing datacenter operators to adopt domain-specific accelerators to train them. These accelerators typically employ densely packed full-precision floating-point arithmetic t ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

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Double-precision floating-point format

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

Multi-ReRAM synapses for artificial neural network training

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

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

AdaptHD: Adaptive Efficient Training for Brain-Inspired Hyperdimensional Computing

Trading off Energy versus Accuracy in Modern Computing Systems

Training DNNs with Hybrid Block Floating Point

Graph Chatbot

Chattez avec Graph Search

Multi-ReRAM synapses for artificial neural network training

Self-Binarizing Networks

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

Training DNNs with Hybrid Block Floating Point

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

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

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

AdaptHD: Adaptive Efficient Training for Brain-Inspired Hyperdimensional Computing

Trading off Energy versus Accuracy in Modern Computing Systems