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04/19/2023
MTrainS: Improving DLRM training efficiency using heterogeneous memories
Recommendation models are very large, requiring terabytes (TB) of memory...
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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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11/09/2022
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure
We present RecD (Recommendation Deduplication), a suite of end-to-end in...
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07/08/2021
First-Generation Inference Accelerator Deployment at Facebook
In this paper, we provide a deep dive into the deployment of inference 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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05/04/2021
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems
Deep learning recommendation systems at scale have provided remarkable g...
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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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02/27/2021
Efficient Soft-Error Detection for Low-precision Deep Learning Recommendation Models
Soft error, namely silent corruption of signal or datum in a computer sy...
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01/13/2021
FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference
Deep learning models typically use single-precision (FP32) floating poin...
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10/21/2020
Mixed-Precision Embedding Using a Cache
In recommendation systems, practitioners observed that increase in the n...
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10/16/2020
Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data
Large scale deep learning provides a tremendous opportunity to improve t...
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11/05/2019
Post-Training 4-bit Quantization on Embedding Tables
Continuous representations have been widely adopted in recommender syste...
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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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05/29/2019
A Study of BFLOAT16 for Deep Learning Training
This paper presents the first comprehensive empirical study demonstratin...
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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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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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05/02/2018
Glow: Graph Lowering Compiler Techniques for Neural Networks
This paper presents the design of Glow, a machine learning compiler for ...
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02/28/2017
Enabling Sparse Winograd Convolution by Native Pruning
Sparse methods and the use of Winograd convolutions are two orthogonal a...
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08/04/2016