127 citations · 1k across the 32 of their papers we have counts for
32 papers
DISCO: Distributed Inference with Sparse Communications
Minghai Qin, Chao Sun, Jaco Hofmann +1
Deep neural networks (DNNs) have great potential to solve many real-world problems, but they usually require an extensive amount of computation and memory. It is of great difficult…
LeaFTL: A Learning-Based Flash Translation Layer for Solid-State Drives
Jinghan Sun, Shaobo Li, Yunxin Sun +3
In modern solid-state drives (SSDs), the indexing of flash pages is a critical component in their storage controllers. It not only affects the data access performance, but also det…
Noisy Computations during Inference: Harmful or Helpful?
Minghai Qin, Dejan Vucinic
We study two aspects of noisy computations during inference. The first aspect is how to mitigate their side effects for naturally trained deep learning systems. One of the motivati…
Training Recurrent Neural Networks against Noisy Computations during Inference
Minghai Qin, Dejan Vucinic
We explore the robustness of recurrent neural networks when the computations within the network are noisy. One of the motivations for looking into this problem is to reduce the hig…
Robustness of Neural Networks against Storage Media Errors
Minghai Qin, Chao Sun, Dejan Vucinic
We study the trade-offs between storage/bandwidth and prediction accuracy of neural networks that are stored in noisy media. Conventionally, it is assumed that all parameters (e.g.…
Inclusive Double Pomeron Exchange at the Fermilab Tevatron pbar-p Collider
D. Acosta
We report a study of at GeV, where is a proton or system of mass-squared GeV. In a sample of events with a leading…