38 citations · 50 across the 7 of their papers we have counts for
9 papers
High-Resolution Convolutional Neural Networks on Homomorphically Encrypted Data via Sharding Ciphertexts
Vivian Maloney, Richard F. Obrecht, Vikram Saraph +2
Recently, Deep Convolutional Neural Networks (DCNNs) including the ResNet-20 architecture have been privately evaluated on encrypted, low-resolution data with the Residue-Number-Sy…
CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery
Kiwan Maeng, Shivam Bharuka, Isabel Gao +8
The paper proposes and optimizes a partial recovery training system, CPR, for recommendation models. CPR relaxes the consistency requirement by enabling non-failed nodes to proceed…
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
Udit Gupta, Samuel Hsia, Vikram Saraph +6
Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure. Thu…
An Empirical Study of Speculative Concurrency in Ethereum Smart Contracts
Vikram Saraph, Maurice Herlihy
We use historical data to estimate the potential benefit of speculative techniques for executing Ethereum smart contracts in parallel. We replay transaction traces of sampled block…
Linearizable Iterators for Concurrent Sets
Archita Agarwal, Zhiyu Liu, Eli Rosenthal +1
This paper proposes a general framework for adding linearizable iterators to a class of data structures that implement set operations. We introduce a condition on set operations, c…
An Algorithmic Approach to the Asynchronous Computability Theorem
Vikram Saraph, Maurice Herlihy, Eli Gafni
The asynchronous computability theorem (ACT) uses concepts from combinatorial topology to characterize which tasks have wait-free solutions in read-write memory. A task can be expr…