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Found 45 papers, 12 papers with code
Deep Learning Recommendation Model for Personalization and Recommendation Systems
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks.
The Architectural Implications of Facebook's DNN-based Personalized Recommendation
The widespread application of deep learning has changed the landscape of computation in the data center.
Exploiting Parallelism Opportunities with Deep Learning Frameworks
State-of-the-art machine learning frameworks support a wide variety of design features to enable a flexible machine learning programming interface and to ease the programmability burden on machine learning developers.
MLPerf Training Benchmark
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML.
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure.
Distributed, Parallel, and Cluster Computing
Developing a Recommendation Benchmark for MLPerf Training and Inference
Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enhance the user experience.
GEVO: GPU Code Optimization using Evolutionary Computation
If kernel output accuracy is relaxed to tolerate up to 1% error, GEVO can find kernel variants that outperform the baseline version by an average of 51. 08%.
AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance
Such execution scaling decision becomes more complicated with the stochastic nature of mobile-cloud execution, where signal strength variations of the wireless networks and resource interference can significantly affect real-time inference performance and system energy efficiency.
Cross-Stack Workload Characterization of Deep Recommendation Systems
Deep learning based recommendation systems form the backbone of most personalized cloud services.
Understanding Capacity-Driven Scale-Out Neural Recommendation Inference
One approach to support this scale is with distributed serving, or distributed inference, which divides the memory requirements of a single large model across multiple servers.
CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery
The paper is the first to the extent of our knowledge to perform a data-driven, in-depth analysis of applying partial recovery to recommendation models and identified a trade-off between accuracy and performance.
Understanding Training Efficiency of Deep Learning Recommendation Models at Scale
The use of GPUs has proliferated for machine learning workflows and is now considered mainstream for many deep learning models.
TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models
TT-Rec achieves 117 times and 112 times model size compression, for Kaggle and Terabyte, respectively.
RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference
Neural personalized recommendation models are used across a wide variety of datacenter applications including search, social media, and entertainment.
RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance
Thus, we design RecPipeAccel (RPAccel), a custom accelerator that jointly optimizes quality, tail-latency, and system throughput.
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale
We share in this paper our search strategies to adapt reference recommendation models to low-precision hardware, our optimization of low-precision compute kernels, and the design and development of tool chain so as to maintain our models' accuracy throughout their lifespan during which topic trends and users' interests inevitably evolve.
SVP-CF: Selection via Proxy for Collaborative Filtering Data
As we demonstrate, commonly-used data sampling schemes can have significant consequences on algorithm performance -- masking performance deficiencies in algorithms or altering the relative performance of algorithms, as compared to models trained on the complete dataset.
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning
Federated learning enables a cluster of decentralized mobile devices at the edge to collaboratively train a shared machine learning model, while keeping all the raw training samples on device.
Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training
Innovations that improve the efficiency and performance of DSI systems and hardware are urgent, demanding a deep understanding of DSI characteristics and infrastructure at scale.
Sustainable AI: Environmental Implications, Challenges and Opportunities
This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware.
Papaya: Practical, Private, and Scalable Federated Learning
Our work tackles the aforementioned issues, sketches of some of the system design challenges and their solutions, and touches upon principles that emerged from building a production FL system for millions of clients.
On Sampling Collaborative Filtering Datasets
We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms.
RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation
EMBs exhibit distinct memory characteristics, providing performance optimization opportunities for intelligent EMB partitioning and placement across a tiered memory hierarchy.
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
Federated learning (FL) is an effective mechanism for data privacy in recommender systems by running machine learning model training on-device.
Infinite Recommendation Networks: A Data-Centric Approach
We leverage the Neural Tangent Kernel and its equivalence to training infinitely-wide neural networks to devise $\infty$-AE: an autoencoder with infinitely-wide bottleneck layers.
Ranked #1 on
Recommendation Systems
on Douban
(AUC metric)
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning
Despite FL's initial success, many important deep learning use cases, such as ranking and recommendation tasks, have been limited from on-device learning.
DataPerf: Benchmarks for Data-Centric AI Development
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems.
Understanding Scaling Laws for Recommendation Models
We show that parameter scaling is out of steam for the model architecture under study, and until a higher-performing model architecture emerges, data scaling is the path forward.
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure
RecD addresses immense storage, preprocessing, and training overheads caused by feature duplication inherent in industry-scale DLRM training datasets.
FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning
Federated learning (FL) has emerged as a solution to deal with the risk of privacy leaks in machine learning training.
FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models
Sequence-based deep learning recommendation models (DLRMs) are an emerging class of DLRMs showing great improvements over their prior sum-pooling based counterparts at capturing users' long term interests.
MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation
Based on our characterization of various embedding representations, we propose a hybrid embedding representation that achieves higher quality embeddings at the cost of increased memory and compute requirements.
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference
We propose three optimization techniques to mitigate sources of inefficiencies, namely (1) Dynamic gating, (2) Expert Buffering, and (3) Expert load balancing.
Green Federated Learning
The rapid progress of AI is fueled by increasingly large and computationally intensive machine learning models and datasets.
READ: Recurrent Adaptation of Large Transformers
However, fine-tuning all pre-trained model parameters becomes impractical as the model size and number of tasks increase.
MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems
Training and deploying large machine learning (ML) models is time-consuming and requires significant distributed computing infrastructures.
GEVO-ML: Optimizing Machine Learning Code with Evolutionary Computation
For the training workloads, GEVO-ML finds a 4. 88% improvement in model accuracy, from 91% to 96%, without sacrificing training or testing speed.
Data Acquisition: A New Frontier in Data-centric AI
As Machine Learning (ML) systems continue to grow, the demand for relevant and comprehensive datasets becomes imperative.
Decoding Data Quality via Synthetic Corruptions: Embedding-guided Pruning of Code Data
Armed with this knowledge, we devise novel pruning metrics that operate in embedding space to identify and remove low-quality entries in the Stack dataset.
Generative AI Beyond LLMs: System Implications of Multi-Modal Generation
As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal information presents unique challenges to quality, performance, and efficiency.
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning
Such fragmentation introduces a new type of data heterogeneity in FL, namely \textit{system-induced data heterogeneity}, as each device generates distinct data depending on its hardware and software configurations.
CHAI: Clustered Head Attention for Efficient LLM Inference
We observe that there is a high amount of redundancy across heads on which tokens they pay attention to.
Croissant: A Metadata Format for ML-Ready Datasets
Data is a critical resource for Machine Learning (ML), yet working with data remains a key friction point.
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
