Apache TVM and Deep Learning Compilation Conference

Wed-Fri, December 2nd-4th 2020, Free Virtual Event.


Apache TVM is an open-source deep learning compiler stack for CPUs, GPUs, and specialized accelerators. It aims to close the gap between the productivity-focused deep learning frameworks, and the performance- or efficiency-oriented hardware backends.

We are excited to announce the 2020 TVM Virtual Conference, December 2-4. The TVM Conference will cover the state of the art of deep learning compilation optimization. We welcome TVM contributors, potential users, UW SAMPL sponsors, collaborators and researchers and practitioners from the broader community. The conference will discuss recent advances in frameworks, compilers, systems and architecture support, security, training and hardware acceleration.


2020 Program

  Dec 2 - Tutorials    
Time Title Speakers Organization
9:00 Introduction to TVM Chris Hoge OctoML
10:15 TVMC: A Command-Line Driver for TVM Leandro Nunes Arm
11:30 Bring Your Own Codegen to TVM Zhi Chen, Cody Yu Amazon Web Services
1:00 uTVM: Running the TVM Stack on Bare Metal Andrew Reusch OctoML
  Dec 3 - Conference    
Time Title Speakers Organization
9:00 Keynote and Community Update Luis Ceze OctoML
    Tianqi Chen OctoML
    Wilson Yu AMD
    Jem Davies Arm
    Kavitha Prasad Sima
    Yida Wang Amazon Web Services
    Jason Knight OctoML
10:00 Break    
10:15 TVM at AWS Yida Wang, Yao Wang, Yong Wu, Haichen Shen, Wei Xiao Amazon Web Services
10:45 TVM at Imagination Ashutosh Parkhi, Jaydeep Patil Imagination Technologies
10:51 TVM at Synopsys & ITRI Kerwin Tung, Chuck Pilkington, Dexian Li, ITRI (Industrial Technology Research Institute)
10:57 A Generic Method to Utilize Vendor-specific AI Accelerator on Android Mobile for TVM Ming-Yu Hung, Ming-Yi Lai MediaTek Inc.
11:03 Extending TVM to Support Custom ML Hardware Joey Chou, Randy Allen SiMa.ai
11:10 Break    
11:20 Advances in Learning Systems Research Joey Gonzalez UC Berkeley
11:50 Lorien: A Scale-Out System and Database for Auto-Tuning Tensor Programs Cody Yu Amazon Web Services
12:10 Lunch Break    
12:50 TVM at ARM Ramana Radhakrishnan Arm
13:20 TVM at OctoML Jason Knight OctoML
13:40 Break    
14:00 Lightning Talks Day 1 Various Various
  Dec 3 - Lightning Talks    
Time Title Speakers Organization
14:00 Graph-Level Scheduling Optimization with Polyhedral Analysis for Tensor Programs Jie Wang Amazon Web Services
14:06 FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems Yuwei Hu Cornell University
14:12 UNIT: Unifying the Compilation of Tensorization Jian Weng UCLA
14:18 RAFT:Accelerating the Tuning Process for AutoTVM HaiWen Fu, XiaoHua Shi, Yuchen Feng Beihang University
14:24 A TVM IR to MLIR Automatic Converter : Bridging TVM with MLIR Ecosystem Jinman Zhao Huawei Technologies Canada/ University of Toronto
14:30 BERT Inference Optimization Using TVM Haichen Shen Amazon Web Services
14:36 Writing Sparse Operators in TIR Tristan Konolige OctoML
14:42 Automatic Differentiation on Tensor Expression Yizhi Liu Amazon Web Services
14:48 Optimizing Automatic Tuning Process of TVM Based on Parallel Genetic Algorithm YuChen Feng, XiaoHua Shi, HaiWen Fu Beihang University
14:54 Overhauling the Onnx Importer to Support Dyanmism Matthew Brookhart, Lily Orth-Smith OctoML
15:00 AdaTune: Adaptive Tensor Program Compilation Made Efficient Menghao Li Microsoft
  Dec 4 - Conference    
Time Title Speakers Organization
9:00 Machine Learning Attacks and how to use KubeFlow Pipelines to Defend David Aronchik Microsoft
9:30 MLIR and MLIR in the TensorFlow Ecosystem Jacques Pienaar Google
10:00 Break    
10:15 Bringing Vitis-AI Hardware Acceleration to TVM for Cloud and Edge Jorn Tuyls Xilinx
10:35 TVM at Alibaba Xiaoyong Liu Alibaba
10:55 End-to-End Performance Assessment of AI Systems with TVM and Virtual Models Michael J. Klaiber Bosch Corporate Research
11:15 Break    
11:30 TVM at Qualcomm Krzysztof Parzyszek Qualcomm
11:50 TVM for Edge Inference at AMD Mei Ye, David Marques AMD
12:10 Lunch Break    
12:50 Ansor : Generating High-Performance Tensor Programs for Deep Learning (An auto-scheduler for TVM) Lianmin Zheng UC Berkeley
13:10 TVM Object System: Multi-language Support for just $19.99 Jared Roesch OctoML
13:20 Break    
13:40 Lightning Talks Day 2 Various Various
  Dec 4 - Lightning Talks    
Time Title Speakers Organization
13:40 AutoTIR - Bringing Automatic Scheduling to TIR Junru Shao OctoML
13:46 Real-time AI on Edge Servers with TVM Kazutaka Morita NTT
13:52 Integrating and Simulating Hardware Accelerators in TVM Luis Vega OctoML
13:58 Tensorization, Scheduling and Allocation for Machine Learning Inference Accelerators Hongbin Zheng, Randy Huang Amazon Web Services
14:04 AArch64 Pre-Quantized Networks Performance Giuseppe Rossini Arm
14:10 Hardware-aware Quantization in TVM Ziheng Jiang, Animesh Jain OctoML, AWS
14:16 Integrating the Arm Ethos-N NPUs into TVM Matthew Barrett Arm
14:22 Running AI WASM Model Securely in Sandbox using TVM and Wasmtime Leon Wang Huawei
14:28 Ethos-U55 : microNPU Support for uTVM Manupa Karunaratne Arm
14:34 Enable TVM QNN on RISC-V with Subword SIMD Computation Jenq-Kuen Lee, Chen Yu-Ri National Tsing-Hua University, Taiwan
14:40 A Generic Framework Based on TVM/VTA for OpenCL-Compatible Cloud Devices Zhang Hao, Li Jaishu Fourth Paradigm Southeast Asia
14:46 HAWQV3: Dyadic Neural Network Quantization Amir Gholami UC Berkely

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