Concept

Decimal floating point

Related publications (32)

Non-perturbative probability distribution function for cosmological counts in cells

We present a non-perturbative calculation of the 1-point probability distribution function (PDF) for the spherically-averaged matter density field. The PDF is represented as a path integral and is evaluated using the saddle-point method. It factorizes into ...

IOP PUBLISHING LTD2019

Advanced Machine Learning Techniques for Self-Interference Cancellation in Full-Duplex Radios

Andreas Peter Burg, Andreas Toftegaard Kristensen

In-band full-duplex systems allow for more efficient use of temporal and spectral resources by transmitting and receiving information at the same time and on the same frequency. However, this creates a strong self-interference signal at the receiver, makin ...

IEEE2019

Impact of Memory Voltage Scaling on Accuracy and Resilience of Deep Learning Based Edge Devices

David Atienza Alonso, Andreas Peter Burg, Miguel Peon Quiros, Andrea Bonetti, Martino Ruggiero, Benoît Walter Denkinger, Flavio Ponzina, Soumya Subhra Basu, Szabolcs Balási

Energy consumption is a significant obstacle to integrate deep learning into edge devices. Two common techniques to curve it are quantization, which reduces the size of the memories (static energy) and the number of accesses (dynamic energy), and voltage s ...

2019

Genius loci e memorie urbane. Il progetto di Ragnar Östberg per l’area di Eldkvarn

Chiara Monterumisi

The paper explores the city of Stockholm, apparently far from the main tracks of other significant European examples due to its peculiar morphological features. The study investigates the Stockholm Town Hall (Stadshuset) and the nearby never-to-be realized ...

2018

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 ...

2018

Training DNNs with Hybrid Block Floating Point

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

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Logistic regression over encrypted data from fully homomorphic encryption

Zhicong Huang

Background: One of the tasks in the 2017 iDASH secure genome analysis competition was to enable training of logistic regression models over encrypted genomic data. More precisely, given a list of approximately 1500 patient records, each with 18 binary feat ...

BMC2018

A Householder-Based Algorithm For Hessenberg-Triangular Reduction

Daniel Kressner, Zvonimir Bujanovic

The QZ algorithm for computing eigenvalues and eigenvectors of a matrix pencil A - lambda B requires that the matrices first be reduced to Hessenberg-triangular (HT) form. The current method of choice for HT reduction relies entirely on Givens rotations re ...

SIAM PUBLICATIONS2018

Mixed-precision architecture based on computational memory for training deep neural networks

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Deep neural networks (DNN) have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image or speech recognition. Training of large DNNs, however, is a computationally in ...

IEEE2018

Giant multiphoton absorption for THz resonances in silicon hydrogenic donors

The absorption of multiple photons when there is no resonant intermediate state is a well-known nonlinear process in atomic vapours, dyes and semiconductors. The N-photon absorption (NPA) rate for donors in semiconductors scales proportionally from hydroge ...

NATURE PUBLISHING GROUP2018

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