2.3k citations · 4.7k across the 64 of their papers we have counts for
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LIT: Block-wise Intermediate Representation Training for Model Compression
Animesh Koratana, Daniel Kang, Peter Bailis +1
Knowledge distillation (KD) is a popular method for reducing the computational overhead of deep network inference, in which the output of a teacher model is used to train a smaller…
Optimizing Data-Intensive Computations in Existing Libraries with Split Annotations
Shoumik Palkar, Matei Zaharia
Data movement between main memory and the CPU is a major bottleneck in parallel data-intensive applications. In response, researchers have proposed using compilers and intermediate…
Beyond Data and Model Parallelism for Deep Neural Networks
Zhihao Jia, Matei Zaharia, Alex Aiken
The computational requirements for training deep neural networks (DNNs) have grown to the point that it is now standard practice to parallelize training. Existing deep learning sys…
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
Cody Coleman, Daniel Kang, Deepak Narayanan +7
Researchers have proposed hardware, software, and algorithmic optimizations to improve the computational performance of deep learning. While some of these optimizations perform the…
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics
Daniel Kang, Peter Bailis, Matei Zaharia
Recent advances in neural networks (NNs) have enabled automatic querying of large volumes of video data with high accuracy. While these deep NNs can produce accurate annotations of…