253 citations · 360 across the 5 of their papers we have counts for
6 papers
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
Maximilian Lam, Jeff Johnson, Wenjie Xiong +11
On-device machine learning (ML) inference can enable the use of private user data on user devices without revealing them to remote servers. However, a pure on-device solution to pr…
Efficient, arbitrarily high precision hardware logarithmic arithmetic for linear algebra
Jeff Johnson
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…
Rethinking floating point for deep learning
Jeff Johnson
Reducing hardware overhead of neural networks for faster or lower power inference and training is an active area of research. Uniform quantization using integer multiply-add has be…
An evaluation of large-scale methods for image instance and class discovery
Matthijs Douze, Hervé Jégou, Jeff Johnson
This paper aims at discovering meaningful subsets of related images from large image collections without annotations. We search groups of images related at different levels of sema…
Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, Hervé Jégou
Similarity search finds application in specialized database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and…
Fast Convolutional Nets With fbfft: A GPU Performance Evaluation
Nicolas Vasilache, Jeff Johnson, Michael Mathieu +3
We examine the performance profile of Convolutional Neural Network training on the current generation of NVIDIA Graphics Processing Units. We introduce two new Fast Fourier Transfo…