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02/16/2023
With Shared Microexponents, A Little Shifting Goes a Long Way
This paper introduces Block Data Representations (BDR), a framework for ...
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03/28/2022
Learning to Collide: Recommendation System Model Compression with Learned Hash Functions
A key characteristic of deep recommendation models is the immense memory...
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10/25/2021
Differentiable NAS Framework and Application to Ads CTR Prediction
Neural architecture search (NAS) methods aim to automatically find the o...
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10/21/2021
Supporting Massive DLRM Inference Through Software Defined Memory
Deep Learning Recommendation Models (DLRM) are widespread, account for a...
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05/26/2021
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale
Tremendous success of machine learning (ML) and the unabated growth in M...
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04/12/2021
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Deep learning recommendation models (DLRMs) are used across many busines...
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08/27/2020
Time-based Sequence Model for Personalization and Recommendation Systems
In this paper we develop a novel recommendation model that explicitly in...
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03/20/2020
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems
Large-scale training is important to ensure high performance and accurac...
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12/30/2019
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
Personalized recommendation systems leverage deep learning models and ac...
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09/25/2019
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
In many real-world applications, e.g. recommendation systems, certain it...
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09/04/2019
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems
Modern deep learning-based recommendation systems exploit hundreds to th...
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06/06/2019
The Architectural Implications of Facebook's DNN-based Personalized Recommendation
The widespread application of deep learning has changed the landscape of...
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05/31/2019
Deep Learning Recommendation Model for Personalization and Recommendation Systems
With the advent of deep learning, neural network-based recommendation mo...
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01/08/2019
Spatial-Winograd Pruning Enabling Sparse Winograd Convolution
Deep convolutional neural networks (CNNs) are deployed in various applic...
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01/07/2019
On the Dimensionality of Embeddings for Sparse Features and Data
In this note we discuss a common misconception, namely that embeddings a...
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11/24/2018
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
The application of deep learning techniques resulted in remarkable impro...
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11/24/2018
On Periodic Functions as Regularizers for Quantization of Neural Networks
Deep learning models have been successfully used in computer vision and ...
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11/14/2018
Bandana: Using Non-volatile Memory for Storing Deep Learning Models
Typical large-scale recommender systems use deep learning models that ar...
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12/18/2017
Parallel Complexity of Forward and Backward Propagation
We show that the forward and backward propagation can be formulated as a...
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12/06/2017
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
Training deep neural networks with Stochastic Gradient Descent, or its v...
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09/16/2017