[ Project overview, Reddit disccusion, Android app, Chrome add-on ]

---

 result   AutoML&systems - Image Classification - MobileNet (all)

 result   AutoML&systems - Image Classification - MobileNet (highlights)

 result   MLPerf Inference v0.5 - Image Classification - crowd-benchmarking

 result   MLPerf Inference v0.5 - Image Classification - MultiStream (number of streams)

 result   MLPerf Inference v0.5 - Image Classification - Offline (samples per second)

 result   MLPerf Inference v0.5 - Image Classification - Server (queries per second)

 result   MLPerf Inference v0.5 - Image Classification - SingleStream (milliseconds per sample)

 result   MLPerf Inference v0.5 - Machine Translation - MultiStream (number of streams)

 result   MLPerf Inference v0.5 - Machine Translation - Offline (samples per second)

 result   MLPerf Inference v0.5 - Machine Translation - Server (queries per second)

 result   MLPerf Inference v0.5 - Machine Translation - SingleStream (milliseconds per sample)

 result   MLPerf Inference v0.5 - Object Detection - crowd-benchmarking

 result   MLPerf Inference v0.5 - Object Detection - MultiStream (number of streams)

 result   MLPerf Inference v0.5 - Object Detection - Offline (samples per second)

 result   MLPerf Inference v0.5 - Object Detection - Server (queries per second)

 result   MLPerf Inference v0.5 - Object Detection - SingleStream (milliseconds per sample)

 result   NNTest (collaboratively benchmarking and optimizing neural network operations)

 result   Quantum Hackathon 2018-06-15 (Variational Quantum Eigensolver on Rigetti) - Solution Convergence

 result   Quantum Hackathon 2018-06-15 (Variational Quantum Eigensolver on Rigetti) - Time to Solution

 result   Quantum Hackathon 2018-10-06 (Variational Quantum Eigensolver on IBM) - Solution Convergence

 result   Quantum Hackathon 2018-10-06 (Variational Quantum Eigensolver on IBM) - Time to Solution

 result   Quantum Hackathon 2019-01-27 (Quantum Machine Learning : Paris)

 result   Quantum Hackathon 2019-03-15 (Quantum Machine Learning : Oxford)

 result   Quantum Open Challenge (Variational Quantum Eigensolver) - Solution Convergence

 result   Quantum Open Challenge (Variational Quantum Eigensolver) - Time to Solution

 result   ReQuEST @ ASPLOS'18 tournament (Pareto-efficient image classification)

 result   SOTA: MLPerf inference benchmark v0.5 results snapshot (closed, Available) for collaborative validation

 result   SOTA: MLPerf inference benchmark v0.5 results snapshot (closed, Preview) for collaborative validation

 result   SOTA: MLPerf inference benchmark v0.5 results snapshot (closed, Research, Development, and Other) for collaborative validation

 result   SOTA: MLPerf inference benchmark v0.5 results snapshot (open, Available) for collaborative validation

 result   SOTA: MLPerf inference benchmark v0.5 results snapshot (open, Research, Development, and Other) for collaborative validation

 result   SOTA: Validating MLPerf inference benchmark v0.5 results (object detection) via cK crowd-benchmarking

 result   Testing crowd-experiments (auto/crowd-tune GCC compiler flags (minimize execution time))

 result   Testing crowd-experiments (auto/crowd-tune GCC compiler flags (minimize execution time, do not degrade code size))

 result   Testing crowd-experiments (auto/crowd-tune LLVM compiler flags (minimize execution time))

 result   Testing crowd-experiments (auto/crowd-tune LLVM compiler flags (minimize execution time, do not degrade code size))

 result   Testing crowd-experiments (auto/crowd-tune OpenCL-based CLBlast (GFLOPs))

 result   Testing crowd-experiments (CK wrapper for OpenCL bug reports from Imperial)

 result   Testing crowd-experiments (crowd-benchmark DNN libraries and models (Caffe - dev))

 result   Testing crowd-experiments (crowd-benchmark DNN libraries and models (Caffe2))

 result   Testing crowd-experiments (crowd-benchmark DNN libraries and models (TensorFlow))

 result   Testing crowd-experiments (crowd-benchmark DNN libraries and models (training - on-going))

 result   Testing crowd-experiments (crowd-benchmark DNN libraries and models using mobile devices)

 result   Testing crowd-experiments (crowd-benchmark DNN libraries and models)

 result   Testing crowd-experiments (crowd-benchmark shared workloads via ARM WA framework)

 result   Testing crowd-experiments (crowdsource modeling of program behavior)

 result   Testing crowd-experiments (Open ACM ReQuEST @ ASPLOS'18 tournament (image classification))