1800 1900 2000 Printing Press Steam Engines Telegraph Electrification Mass Steel Production Mass Production & Assembly Lines Internal Combustion Engine Flight Synthetic Fertilizer Transistors autonomous robotic vacuum cleaner that can navigate homes, is launched 10/05: A Stanford team build a driverless car named Stanley; it completes a 132-mile course, winning the DARPA AI and robotics to free humanity from menial repetitive work and to dramatically increase the production of goods and services could presage an era of peace and plenty. The capacity to accelerate scientific

Research Team advances the foundations of AI, developing new models and capabilities. Our Applied Team turns those models into products, like ChatGPT Enterprise and our API. And our Deployment Team takes These evals—and others—gave Morgan Stanley the confidence to start rolling the use cases 
 into production. 6 AI in the EnterpriseHow it’s going Today, 98% of Morgan Stanley advisors use OpenAI every explains why a candidate’s background or previous work experience makes the job 
 a good fit. The Indeed team tested the previous job matching engine against the GPT-powered version with the new, customized

TVM community and contribute new infrastructure and features. octom|.ai @octoml Q octoML Founding Team - The Octonauts - 四人全外日 Luis Ceze Jason Knight Tianqi Infrastructure Improvements to TVM o_uTVM: support for microcontrollers in TVM o_ Virtual Machine and dynamic NNs support (w/ AWS folks) o_ Improved NLP support, with focus on transformers QQ octoML Core loops e Support for different integer division modes, floor division and truncating division. e Unified Object and Node system for TVM runtime o Lays groundwork forimproved multi-language support for expPosing

instructions Use existing documents When creating routines, use existing operating procedures, support scripts, or policy documents to create LLM-friendly routines. In customer service for example, routines both sales and support: Python 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 from agents import Agent, Runner technical_support_agent = Agent( ] ) order_management_agent = Agent( name= , instructions=( "Technical Support Agent", "You provide expert assistance with resolving technical issues, system outages, or product

2018.12 | 2019.6 ee 2019.10 Alios TVM Team Set up TFLite Quantized Support 1.61X MobilenetVl TFlite TV Hexagon DSP Inference Engine DSP (Qualcomm) PART TWO Alios TVM @ ARM CPU AiOS 1驱动万物智能 Alios TVMQOARM CPU 。 Support TFLite ( Open Source and Upstream Master ) 。， Optimize on INT8 & FP32 AiiOS ! 驱动万物智能 Alios TVM could generate HVX instruction 。， Add one Hexagon runtimes named as libtvm_hexagon_runtime.so to support parallel. 。 Could run end-to-end TFLite Mobilenet V2 quantized model on Simulator / Device. /NiiOS

outperform GPT-4-0613 and ERNIEBot 4.0, solidifying the position of our models in the top-tier LLMs that support Chinese. Specifically, DeepSeek-V2 Chat (RL) shows remarkable performance in Chinese language understanding our next step is to achieve performance on par with GPT-4 in our upcoming release. • Our alignment team continuously strives to enhance our models, aiming to develop a model that is not only helpful but society. • Currently, DeepSeek-V2 is designed to support the text modality exclusively. In our forward-looking agenda, we intend to enable our model to support multiple modalities, enhancing its versatility

reserved. Amazon Trademark 1. AWS has most TVM contributors from industry. 2. We plan to build TVM team in China, based in Shanghai, Beijing and Shenzhen. 1. Applied Scientist and SDE positions 2. Internship

(Py/Java/Go) lib = tvm.module.load("mylib.so") func = lib["npufunction0"] func(a, b) Automatic RPC Support remote = tvm.rpc.connect(board_url, port) remote.upload("mylib.so") remote_mod = remote.load_module(“mylib runtime for dynamic models Credit: Jared Roesch, Haichen Shen et.aluTVM: TVM on bare-metal Devices Support bare-metal J-TAG devices, no OS is needed ARM Cortex-M RISC-V Credit: Logan WeberuTVM upcoming: TPUsTensorization Challenge Compute primitives scalar vector tensor Challenge: Build systems to support emerging tensor instructionsTensorization Challenge C = tvm.compute((m, n), 
 lambda y, x:

Driver ● Arm Compute Library has been integrated by: ○ MATLAB Coder ○ ONNX RuntimeArm platform support in TVM upstream IPs Target Hardware/Model Options Codegen CPU arm_cpu pixel2 (snapdragon 835), t860) N/A opencl bifrost hikey960 (mali g71) N/A FPGA vta pynq, ultra96 N/A sdaccel Out-of-tree support or WIP: Hexagon DSP (via llvm), Ascend NPU, and more Green: Linaro 96BoardsLinaro for TVM ● Linaro Linaro AI/ML group can be a good fit for TVM collaborations on Arm based platforms to support more devices with various accelerator configurations (from microcontrollers to HPC) by working together with

models© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Support dynamic model in TVM ● Support Any-dim in typing ● Use shape function to compute the type at runtime ● Virtual Inc. or its Affiliates. All rights reserved. Dynamic codegen: op dispatch (proposal) ● Goal: support codegen for dynamic shape ● Challenges ○ Single kernel performs poor across different shapes ○