3 citations · 5 across the 10 of their papers we have counts for
11 papers
On Coded Caching Systems with Decentralized Linear Coding Placement
Yinbin Ma, Daniela Tuninetti
Coded caching is a technique that leverages locally cached contents at the end users to reduce the network's peak-time communication load. Coded caching has been shown to achieve s…
Computer-aided Characterization of Fundamental Limits of Coded Caching with Linear Coding
Niccolò Brembilla, Yinbin Ma, Pietro Belotti +2
Inspired by prior work by Tian and by Cao and Xu, this paper presents an efficient computer-aided framework to characterize the fundamental limits of coded caching systems under th…
An Achievable Scheme for the K-user Linear Computation Broadcast Channel
Yinbin Ma, Daniela Tuninetti
This paper presents a new achievable scheme for the K-user Linear Computation Broadcast Channel (K-LCBC). A K-LCBC comprises data stored on a server and K users, each aiming to ret…
A Novel Scheme for Coded Caching with Coded Placement in Small Memory Regime
Yinbin Ma, Daniela Tuninetti
This paper presents a novel achievable scheme for coded caching systems with files and users, specifically when . This new scheme employs linear coding both durin…
On Coded Caching Systems with Offline Users, with and without Demand Privacy against Colluding Users
Yinbin Ma, Daniela Tuninetti
Coded caching is a technique that leverages locally cached contents at the end users to reduce the network's peak-time communication load. Coded caching has been shown to achieve s…
New optimal trade-off point for coded caching systems with limited cache size
Yinbin Ma, Daniela Tuninetti
This paper presents a new achievable scheme for coded caching systems with files, users, and cache size . The sche…
Demand Privacy in Hotplug Caching Systems
Yinbin Ma, Daniela Tuninetti
Coded caching, introduced by Maddah-Ali and Niesen (MAN), is a model where a server broadcasts multicast packets to users with a local cache that is leveraged so as to reduce the p…
AutoShard: Automated Embedding Table Sharding for Recommender Systems
Daochen Zha, Louis Feng, Bhargav Bhushanam +7
Embedding learning is an important technique in deep recommendation models to map categorical features to dense vectors. However, the embedding tables often demand an extremely lar…
On Coded Caching Systems with Offline Users
Yinbin Ma, Daniela Tuninetti
Coded caching is a technique that leverages locally cached contents at the users to reduce the network's peak-time communication load. Coded caching achieves significant performanc…
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang +50
Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand…
A General Coded Caching Scheme for Scalar Linear Function Retrieval
Yinbin Ma, Daniela Tuninetti
Coded caching aims to minimize the network's peak-time communication load by leveraging the information pre-stored in the local caches at the users. The original single file retrie…