| Date | Paper Title | Presenter | Notes |
|---|---|---|---|
| 07.14 | AKG: automatic kernel generation for neural processing units using polyhedral transformations (PLDI 2021) | Yuxian Qiu | Slides |
| 07.21 | Floating-Point Format and Quantization for Deep Learning Computation | Cong Guo | |
| 07.28 | P-OPT: Practical Optimal Cache Replacement for Graph Analytics | Yangjie Zhou | Slides |
| 08.04 | Rubik: A Hierarchical Architecture for Efficient Graph Neural Network Training | Zhihui Zhang | |
| 08.11 | A Useful Tool CKA: Similarity of Neural Network Representations Revisited and It's application: Uncovering How Neural Network Representations Vary with Width and Depth | Zhengyi Li | Slides |
| 08.18 | Ansor: Generating High-Performance Tensor Programs for Deep Learning | Zihan Liu | Slides |
| Date | Paper Title | Presenter | Notes |
|---|---|---|---|
| 10.11 | Adaptive numeric type for DNN quantization | Cong Guo | |
| 10.18 | Compiling Graph Applications for GPUs with GraphIt | Yangjie Zhou | Slides |
| 11.01 | TENET: A Framework for Modeling Tensor Dataflow Based on Relation-centric Notation | Zihan Liu | Slides |
| 11.08 | Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity | Zhengyi Li | Slides (code: zdea) |
| 11.22 | Dynamic Tensor Rematerialization Checkmate: Breaking The Memory Wall with Optimal Tensor Rematerialization |
Yue Guan | Slides Slides |
| 11.29 | GraphPulse: An Event-Driven Hardware Accelerator for Asynchronous Graph Processing | Zhihui Zhang | Presentation |
| 12.06 | CheckFreq: Frequent, Fine-Grained DNN Checkpointing | Guandong Lu | Slides |
| 12.13 | PipeDream: generalized pipeline parallelism for DNN training | Runzhe Chen | Slides |
| 12.20 | Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters | Yakai Wang | Slides |
| Date | Paper Title | Presenter | Notes |
|---|---|---|---|
| 3.10 | Speculation Attack: Meltdown, Spectre, Pinned-Loads | Zihan Liu | Slides |
| 3.24 | SparTA: Deep-Learning Model Sparsity via Tensor-with-Sparsity-Attribute | Yue Guan | |
| 3.31 | Fast and Efficient Tensor Compilation for Deep Learning | Yijia Diao | |
| 4.07 | Adaptable Register File Organization for Vector Processors | Zhihui Zhang | |
| 4.14 | CORTEX: A COMPILER FOR RECURSIVE DEEP LEARNING MODELS | Yangjie Zhou | Slides |
| 4.21 | Zero-Knowledge Succinct Non-Interactive Argument of Knowledge | Shuwen Lu | Slides |
| 5.05 | Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning | Runzhe Chen | Slides |
List Contributed by Jingwen Leng
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