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Found 11 papers, 7 papers with code
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100, 000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy.
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference
Multiparty computation approaches to secure neural network inference traditionally rely on garbled circuits for securely executing nonlinear activation functions.
The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage
The People's Speech is a free-to-download 30, 000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-BY-SA (with a CC-BY subset).
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
We show that aggregated model updates in federated learning may be insecure.
Widening Access to Applied Machine Learning with TinyML
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation.
Quantized Neural Network Inference with Precision Batching
We present PrecisionBatching, a quantized inference algorithm for speeding up neural network execution on traditional hardware platforms at low bitwidths without the need for retraining or recalibration.
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
We believe that this is the first of many future works on enabling computationally energy-efficient and sustainable reinforcement learning.
Word2Bits - Quantized Word Vectors
Word vectors require significant amounts of memory and storage, posing issues to resource limited devices like mobile phones and GPUs.
Cataloging the Visible Universe through Bayesian Inference at Petascale
We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia.
Distributed, Parallel, and Cluster Computing Instrumentation and Methods for Astrophysics 85A35, 68W10, 62P35 J.2; D.1.3; G.3; I.2; D.2
CYCLADES: Conflict-free Asynchronous Machine Learning
We present CYCLADES, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting.
Speeding Up Distributed Machine Learning Using Codes
We focus on two of the most basic building blocks of distributed learning algorithms: matrix multiplication and data shuffling.
