Week |
| #1 | Sep. 17: Introduction |
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The Task of the Referee |
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| #2 | Sep. 24: Benchmarks and Analytics |
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MLPerf training benchmark [Google et al., MLSys 20] |
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MLPerf Inference Benchmark [Harvard et al., ISCA 20] |
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| #3 | Oct. 01: Systems & ML |
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A Systematic Methodology for Analysis of Deep Learning Hardware and Software Platforms [Harvard, MLSys 2020] |
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The Case for Learned Index Structures [MIT and Google, SIGMOD 18] |
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| #4 | Oct. 08: Federated Learning |
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Towards federated learning at scale: System design [Google, MLSys 19] |
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The Non-IID Data Quagmire of Decentralized Machine Learning [ETHZ and CMU, ICML 20] |
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| #5 | Oct. 15: Decentralized Learning |
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SwarmSGD: Scalable Decentralized SGD with Local Updates [IST Austria, Arxiv 2020] |
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Byzantine-Resilient Multi-Agent Optimization [MIT, IEEE 2020] |
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| #6 | Oct. 22: Deep Learning with Low-precision Computations |
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Training DNNs with Hybrid Block Floating Point [EPFL, NeurIPS 18] |
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Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks [Xilinx, MLSys 20] |
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| #7 | Oct. 29: Training with Low-precision Gradients |
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Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training [Tsinghua & Nvidia, ICLR 18] |
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PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning [EPFL, arXiv 20] |
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| #8 | Nov. 05: Distributed Imagenet & Transformers Training |
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Beyond Data and Model Parallelism for Deep Neural Networks [Stanford, MLSys 19] |
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Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism [Nvidia, arXiv 20] |
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| #9 | Nov. 12: Training with Model Parallelism |
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PipeDream: generalized pipeline parallelism for DNN training [Mircosoft et al., SOSP 19] |
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Decoupled Parallel Backpropagation with Convergence Guarantee [Pittsburgh, ICML 18] |
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| #10 | Nov. 19: Neural Architecture Search |
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ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware [MIT, ICLR 19] |
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MnasNet: Platform-Aware Neural Architecture Search for Mobile [Google, CVPR 19] |
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| #11 | Nov. 26: Domain Specific Languages for ML |
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Blink: Fast and Generic Collectives for Distributed ML [Microsoft et al., MLSys 20] |
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TVM: An Automated End-to-End Optimizing Compiler for Deep Learning [Washington, OSDI 18] |
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| #12 | Dec. 03: ML inference at scale |
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Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective [Facebook, HPCA 18] |
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The Architectural Implications of Facebookâs DNN-based Personalized Recommendation [Facebook, HPCA 20] |
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| #13 | Dec. 10: Hardware Accelerators for Deep Learning |
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Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture [UCB et al., MICRO 19] |
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Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices [MIT, IEEE JETCAS 19] |
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| #14 | Dec. 17: Security |
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Game of Threads: Enabling Asynchronous Poisoning Attacks [UIUC, ASPLOS 20] |
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Stealing Machine Learning Models via Prediction APIs [EPFL, USENIX Security, 16] |
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