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 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 Affiliates. All rights reserved. Relay virtual machine Relay Executable relay.vm.compile Relay Object (hardware independent) Code segment VM Func 0 VM Func 1 ... VM Func N Data segment Const 0

artificial intelligence technology evolution is indeed unprecedented, as supported by the data. This document is filled with user, usage and revenue charts that go up-and-to-the-right… often supported by spending competition – especially related to China and USA tech developments – is acute. The outline for our document is on the next page, followed by eleven charts that help illustrate observations that follow. Change Happening Faster Than Ever? Yes, It Is • AI User + Usage + CapEx Growth = Unprecedented • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer

implemented using LLVM. It is multi-paradigm, combining features of imperative, functional, and object-oriented programming. Julia provides ease and expressiveness for high-level numerical computing Although one sometimes speaks of dynamic languages as being "typeless", they are definitely not. Every object, whether primitive or user-defined, has a type. The lack of type declarations in most dynamic lan- specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" an operation as in traditional object-oriented dispatch. Operators

implemented using LLVM. It is multi-paradigm, combining features of imperative, functional, and object-oriented programming. Julia provides ease and expressiveness for high-level numerical computing Although one sometimes speaks of dynamic languages as being "typeless", they are definitely not. Every object, whether primitive or user-defined, has a type. The lack of type declarations in most dynamic lan- specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" an operation as in traditional object-oriented dispatch. Operators

implemented using LLVM. It is multi-paradigm, combining features of imperative, functional, and object-oriented programming. Julia provides ease and expressiveness for high-level numerical computing Although one sometimes speaks of dynamic languages as being "typeless", they are definitely not. Every object, whether primitive or user-defined, has a type. The lack of type declarations in most dynamic lan- specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" an operation as in traditional object-oriented dispatch. Operators

implemented using LLVM. It is multi-paradigm, combining features of imperative, functional, and object-oriented programming. Julia provides ease and expressiveness for high-level numerical computing Although one sometimes speaks of dynamic languages as being "typeless", they are definitely not. Every object, whether primitive or user-defined, has a type. The lack of type declarations in most dynamic lan- specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" an operation as in traditional object-oriented dispatch.CHAPTER 1.

implemented using LLVM. It is multi-paradigm, combining features of imperative, functional, and object-oriented programming. Julia provides ease and expressiveness for high-level numerical computing Although one sometimes speaks of dynamic languages as being "typeless", they are definitely not. Every object, whether primitive or user-defined, has a type. The lack of type declarations in most dynamic lan- specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" an operation as in traditional object-oriented dispatch.CHAPTER 1.

implemented using LLVM. It is multi-paradigm, combining features of imperative, functional, and object-oriented programming. Julia provides ease and expressiveness for high-level numerical computing Although one sometimes speaks of dynamic languages as being "typeless", they are definitely not. Every object, whether primitive or user-defined, has a type. The lack of type declarations in most dynamic lan- specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" an operation as in traditional object-oriented dispatch.CHAPTER 1.

implemented using LLVM. It is multi-paradigm, combining features of imperative, functional, and object-oriented programming. Julia provides ease and expressiveness for high-level numerical computing Although one sometimes speaks of dynamic languages as being "typeless", they are definitely not. Every object, whether primitive or user-defined, has a type. The lack of type declarations in most dynamic lan- specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" an operation as in traditional object-oriented dispatch.CHAPTER 1.

implemented using LLVM. It is multi-paradigm, combining features of imperative, functional, and object-oriented programming. Julia provides ease and expressiveness for high-level numerical computing Although one sometimes speaks of dynamic languages as being "typeless", they are definitely not. Every object, whether primitive or user-defined, has a type. The lack of type declarations in most dynamic lan- specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" an operation as in traditional object-oriented dispatch.CHAPTER 1.