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PUBLICATIONS

Single Event Effect Assessment of a 1-Mbit Commercial MRAM

Single event effect susceptibility of a 1-Mbit commercial MRAM was experimentally evaluated. The memory exhibited SEFIs when operated in a dynamic mode with an LET threshold of 2.29 MeV.cm2/mg and a saturated cross section of 2.2x10-4 cm2/device. The memory was not sensitive to SEL, SEU or MBUs.

Video thumbnail of Field Guide to Machine Learning, Lesson 1: Problem Definition
VIDEOS

Field Guide to Machine Learning, Lesson 1: Problem Definition

The Facebook Field Guide to Machine Learning is a six-part video series developed by the Facebook ads machine learning team. The series shares best real-world practices and provides practical tips about how to apply machine-learning capabilities to real-world problems....

PUBLICATIONS

12-in-1: Multi-Task Vision and Language Representation Learning

Much of vision-and-language research focuses on a small but diverse set of independent tasks and supporting datasets often studied in isolation; however, the visually grounded language understanding skills required for success at these tasks overlap significantly. In this work, we investigate these relationships between vision-and-language tasks by developing a large-scale, multi-task training regime

PUBLICATIONS

Efficient, arbitrarily high precision hardware logarithmic arithmetic for linear algebra

The logarithmic number system (LNS) is arguably not broadly used due to exponential circuit overheads for summation tables relative to arithmetic precision. Methods to reduce this overhead have been proposed, yet still yield designs with high chip area and power requirements. Use remains limited to lower precision or high multiply/add ratio cases, while much of linear algebra (near 1:1 multiply/add ratio) does not qualify. We present a dual-base approximate logarithmic arithmetic comparable to floating point in use, yet unlike LNS it is easily fully pipelined, extendable to arbitrary precision with O(n^2) overhead, and energy efficient at a 1:1 multiply/add ratio.

PUBLICATIONS

BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task

This paper presents our submission to the WMT2020 Shared Task on Quality Estimation (QE)1 . We participate in Task 1 and Task 2 focusing on sentence-level prediction. We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glassbox approaches that leverage various indicators that can be extracted from the neural MT systems.