Stack High-Level Differentiable IR Tensor Expression and Optimization Search Space LLVM, CUDA, Metal VTA Edge FPGA Cloud FPGA ASIC Optimization AutoTVM Device FleetExisting Deep Learning Frameworks 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:

Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) Your Dispatcher Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) Your Dispatcher Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) Your Dispatcher

y, width=2). This is useful in situations where the keyword name is computed at runtime. When a bare identifier or dot expression occurs after a semicolon, the keyword argument name is implied by the in ...Base (which has nothing to do with Julia's Base module). Default top-level definitions and bare modules Modules automatically contain using Core, using Base, and definitions of the eval and include non-constant global variables in Julia, especially in tight loops. Since you can write close-to-metal code in Julia (unlike Python), the effect of globals can be drastic (see Performance Tips). • In

y, width=2). This is useful in situations where the keyword name is computed at runtime. When a bare identifier or dot expression occurs after a semicolon, the keyword argument name is implied by the in ...Base (which has nothing to do with Julia's Base module). Default top-level definitions and bare modules Modules automatically contain using Core, using Base, and definitions of the eval and include non-constant global variables in Julia, especially in tight loops. Since you can write close-to-metal code in Julia (unlike Python), the effect of globals can be drastic (see Performance Tips). • In

y, width=2). This is useful in situations where the keyword name is computed at runtime. When a bare identifier or dot expression occurs after a semicolon, the keyword argument name is implied by the in ...Base (which has nothing to do with Julia's Base module). Default top-level definitions and bare modules Modules automatically contain using Core, using Base, and definitions of the eval and include non-constant global variables in Julia, especially in tight loops. Since you can write close-to-metal code in Julia (unlike Python), the effect of globals can be drastic (see Performance Tips). • In

y, width=2). This is useful in situations where the keyword name is computed at runtime. When a bare identifier or dot expression occurs after a semicolon, the keyword argument name is implied by the in ...Base (which has nothing to do with Julia's Base module). Default top-level definitions and bare modules Modules automatically contain using Core, using Base, and definitions of the eval and include non-constant global variables in Julia, especially in tight loops. Since you can write close-to-metal code in Julia (unlike Python), the effect of globals can be drastic (see Performance Tips). • In

y, width=2). This is useful in situations where the keyword name is computed at runtime. When a bare identifier or dot expression occurs after a semicolon, the keyword argument name is implied by the in ...Base (which has nothing to do with Julia's Base module). Default top-level definitions and bare modules Modules automatically contain using Core, using Base, and definitions of the eval and include non-constant global variables in Julia, especially in tight loops. Since you can write close-to-metal code in Julia (unlike Python), the effect of globals can be drastic (see Performance Tips). • In

y, width=2). This is useful in situations where the keyword name is computed at runtime. When a bare identifier or dot expression occurs after a semicolon, the keyword argument name is implied by the world age. See the manual chapter on world age for more details. Default top-level definitions and bare modules Modules automatically contain using Core, using Base, and definitions of the eval and include non-constant global variables in Julia, especially in tight loops. Since you can write close-to-metal code in Julia (unlike Python), the effect of globals can be drastic (see Performance Tips). • In

y, width=2). This is useful in situations where the keyword name is computed at runtime. When a bare identifier or dot expression occurs after a semicolon, the keyword argument name is implied by the world age. See the manual chapter on world age for more details. Default top-level definitions and bare modules Modules automatically contain using Core, using Base, and definitions of the eval and include non-constant global variables in Julia, especially in tight loops. Since you can write close-to-metal code in Julia (unlike Python), the effect of globals can be drastic (see Performance Tips). • In

y, width=2). This is useful in situations where the keyword name is computed at runtime. When a bare identifier or dot expression occurs after a semicolon, the keyword argument name is implied by the world age. See the manual chapter on world age for more details. Default top-level definitions and bare modules Modules automatically contain using Core, using Base, and definitions of the eval and include non-constant global variables in Julia, especially in tight loops. Since you can write close-to-metal code in Julia (unlike Python), the effect of globals can be drastic (see Performance Tips). • In