Metadata

This paper was put on arxiv on May 2017, and is authored by Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana at Columbia and Lehigh Universities. The paper proposes a framework to automatically generate inputs that trigger/cover different parts of a Deep Neural Network (DNN) for inference and identify incorrect behaviors. It is easy to see the motivation for high-coverage testing of DNNs. We use DNN inference for safety-critical tasks such as self-driving cars; A DNN gives us results, but we don't know how it works, and how much it works. DNN inference is opaque and we don't have any guarantee that it will not mess up spectacularly in a slightly different input then the ones it succeeded. There are too many corner cases to consider for input based testing, and rote testing will not be able to cover all bases. DeepXplore goes about DNN inference testing in an intelligent manner. It shows that finding inputs triggering differential behaviors while achieving high neuron cover...

This paper is by Frank McSherry, Michael Isard, Derek G. Murray and appeared in HotOS 2015. The authors are all listed as unaffiliated because this is around the time where Microsoft Research Silicon Valley lab was closed, where they used to work. Michael and Derek are at Google working on TensorFlow framework, but Frank McSherry is still at large and unaffiliated. Frank has a great blog, where you will learn more than you ever wanted to know about dataflow, Rust, differential privacy, and the art of influencing people and making friends.  COST, defined per system for a problem, is the configuration required before the system outperforms a competent single-threaded implementation. They show that many big data systems have surprisingly large COST, often hundreds of cores. Let's repeat this again: some single threaded implementations were found to be more than an order of magnitude faster than published results (at SOSP/OSDI!) for systems using hundreds of cores. The paper...

This paper is by Yiping Kang, Johann Hauswald, Cao Gao, Austin Rovinski, Trevor Mudge, Jason Mars, and Lingjia Tang from University of Michigan, and appeared at ASPLOS 17. In Deep Learning (DL), you have a long, computation-intensive training phase where you micro-fiddle/fudge the model parameters until you get desired accuracy. Then you deploy this optimized model parameters (i.e., the Deep Neural Network [DNN])for inference with real-world inputs. The paper is about this inference/serving layer of DL. In the serving layer, the input goes through the DL with the tuned model parameters activating some subset of neurons at each layer and finally activating the correct neuron[s] at the output layer. This can still be a computation intensive process as the model has millions of parameters, and you apply matrix multiplication layer after layer. So this serving layer still has many juicy problems to work on. A very relevant problem is that executing inference at the mobile can be slow...