rights reserved. Presenter: Haichen Shen, Yao Wang Amazon SageMaker Neo, Deep Engine Science Dynamic Model in TVM AWS AI© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Models with while loop Limitation of TVM/graph runtime ● Cannot compile and run dynamic models© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Support dynamic model in TVM ● Support Any-dim new runtime for Relay ● Dynamic codegen (WIP) ○ Kernel dispatch for a single op ○ Graph dispatch for a (sub-)graph In collaboration with Jared Roesch, Zhi Chen, Wei Chen© 2019, Amazon Web Services,

Efficient Mixture-of-Experts Language Model DeepSeek-AI research@deepseek.com Abstract We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models. The model checkpoints are available at h t t p s : / / g i t h u b . c o m / d e e p s e e k - a i / D e e p Work 21 A Contributions and Acknowledgments 27 B DeepSeek-V2-Lite: A 16B Model Equipped with MLA and DeepSeekMoE 29 2 B.1 Model Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

initially developed for the 3D model viewer to make it possible to support more types of 3D models without requiring major changes to the KiCad source for each new model type supported. The plugin framework domain and therefore requires an interface unique to that domain. For example, the 3D model plugins load 3D model data from files and translate that data into a format which can be displayed by the 3D render this type of 3D model. In some cases a plugin may not yet provide a visual model and must return false. */ bool CanRender( void ); /* Load the specified model and return a pointer to

initially developed for the 3D model viewer to make it possible to support more types of 3D models without requiring major changes to the KiCad source for each new model supported. The plugin framework domain and therefore requires an interface unique to that domain. For example, the 3D model plugins load 3D model data from files and translate that data into a format which can be displayed by the 3D render this type of 3D model. In some cases a plugin may not yet provide a visual model and must return false. */ bool CanRender( void ); /* Load the specified model and return a pointer to

initially developed for the 3D model viewer to make it possible to support more types of 3D models without requiring major changes to the KiCad source for each new model supported. The plugin framework domain and therefore requires an interface unique to that domain. For example, the 3D model plugins load 3D model data from files and translate that data into a format which can be displayed by the 3D of 3D model. In some cases a plugin may not yet provide a visual model and must return false. */ bool CanRender( void ); /* Load the specified model and return a pointer to its visual model data */

initially developed for the 3D model viewer to make it possible to support more types of 3D models without requiring major changes to the KiCad source for each new model type supported. The plugin framework domain and therefore requires an interface unique to that domain. For example, the 3D model plugins load 3D model data from files and translate that data into a format which can be displayed by the 3D of 3D model. In some cases a plugin may not yet provide a visual model and must return false. */ bool CanRender( void ); /* Load the specified model and return a pointer to its visual model data */

CPU Layers FPGA Layers Runtime Image Model Weights Calibration Set Quantizer Compiler Tensor Graph Optimization Framework Tensor Graph to Xilinx Tensor Graph Frontend Deep Learning Frameworks https://github https://github.com/xilinx© Copyright 2018 Xilinx TVM as Unified ML Front End >> 6 Relay (and NNVM) Graph Parser XIR Compiler Quantizer Partitioner @relay.transform.module_pass(opt_level=4) class AccelModule:© supported/not supported, pattern matching graph colorization - Choices how to partition especially for multi-branch networks (i.e. YOLOv3, SSD)© Copyright 2018 Xilinx TVM Graph Partitioning/Fusion >> 8 Subgraph

ingests a FP32 graph and a small dataset • Finds suitable quantization scale • Produces a quantized graph • Compiling Pre-quantized models – QNN Dialect • TVM ingests a pre-quantized graph in TFLite or Affiliates. All rights reserved. TVM Overview Framework Graph Mxnet TF …. parsers Relay Graph Target-independent Relay passes Target-optimized graph Target-dependent Relay passes Intel x86 ARM CPU targets AutoTVM – Tuning the kernels Optimized Binary Codegen – LLVM, Cuda, C, … Framework Parsers Graph level optimizations Tensor-level optimizations Machine code generation© 2019, Amazon Web Services

3D modeling and animation tools within the reach of the general computing public. NaN's business model involved providing commercial products and services around Blender. In 1999 NaN attended its first Integrated stereo/multi-view pipeline, Smooth Corrective modifier and new developmental dependency graph. 2.76 -- November 2015: Pixar OpenSubdiv support, Viewport and File Browser performance boost, node to organize objects. Other improvements: Cycles, Modeling, Animation, Import/Export, Dependency Graph. 2.81 -- November 2019: Revamped sculpting tools, Cycles OptiX accelerated rendering, denoising

3D modeling and animation tools within the reach of the general computing public. NaN’s business model involved providing commercial products and services around Blender. In 1999 NaN attended its first Integrated stereo/multi-view pipeline, Smooth Corrective modifier and new developmental dependency graph. 2.76 – November 2015: Pixar OpenSubdiv support, Viewport and File Browser performance boost, node to organize objects. Other improvements: Cycles, Modeling, Animation, Import/Export, Dependency Graph. 2.81 – November 2019: Revamped sculpting tools, Cycles OptiX accelerated rendering, denoising,