Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) Your Dispatcher Target Device General Devices (CPU/GPU/FPGA) Mark supported Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) Your Dispatcher Target Device General Devices (CPU/GPU/FPGA) Mark supported Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) Your Dispatcher Target Device General Devices (CPU/GPU/FPGA) Mark supported

goingUnified Runtime For Heterogeneous Devices CUDA Driver NPU Driver Device Drivers External Runtimes NPUModule CUDAModule TFModule tvm::runtime::Module GetFunction(string) -> tvm::runtime::PackedFunc nc SaveToBinary/LoadFromBinary Runtime Module Interface SubclassesUnified Runtime Benefit mod.export_library("mylib.so") Unified library packaging Free API (Py/Java/Go) lib = tvm.module.load("mylib remote_b)Virtual Machine: Supporting Dynamic Workload Dynamic shape workloads More runtime objects: Arrays, Tuples, Trees, ADTs Minimum runtime for dynamic models Credit: Jared Roesch, Haichen Shen et.aluTVM: TVM

truncating division. e Unified Object and Node system for TVM runtime o Lays groundwork forimproved multi-language support for expPosing runtime, and |IRs. QQ octoML Unified Object Protocol vm::Object NDArray | Rd | tuplelclosure AST Nodes Cross language suppPort Easy to introduce new runtime objects (trees, graphs) Direct access from other languages QQ octoML HTVM Overview *。 Plug directly emit code for microcontrollers that is device- agnostic AuroTYM QQ octoML AutoTVM on HTVM DTYM Runtime send program 较 ,we 人 Interace Optimize TVM operators on microcontrollers by making use of

usually with same runtime operations  Beware: Avoid speedup-only results (need absolute numbers for grounding)  #2 Additional Baselines  Alternative systems w/ different runtime and compiler  Usually Experiments and Result Presentation Runtime What are the units? Where are the tics? Runtime [s] 80 90 100 Yours Mine This is a misleading y axis Runtime [s] 0 50 100 Yours Mine Aha, so

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 5.2.8. Passing JVM runtime options to OFBiz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . background Start OFBiz in the background by forking it to a new process and redirecting the output to runtime/logs/console.log gradlew "ofbizBackground --start" OR gradlew ofbizBackground You can also offset example: gradlew "ofbizBackground --start --portoffset 10000" 5.2.8. Passing JVM runtime options to OFBiz You can pass JVM runtime options by specifying the project property -PjvmArgs. gradlew ofbiz -PjvmArgs="-Xms1024M

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 5.2.8. Passing JVM runtime options to OFBiz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . background Start OFBiz in the background by forking it to a new process and redirecting the output to runtime/logs/console.log gradlew "ofbizBackground --start" OR gradlew ofbizBackground You can also offset example: gradlew "ofbizBackground --start --portoffset 10000" 5.2.8. Passing JVM runtime options to OFBiz You can pass JVM runtime options by specifying the project property -PjvmArgs. gradlew ofbiz -PjvmArgs="-Xms1024M

neural networks >> 4© Copyright 2018 Xilinx Inference Flow >> 5 MxNet CPU Layers FPGA Layers Runtime Image Model Weights Calibration Set Quantizer Compiler Tensor Graph Optimization Framework Tensor 11 Calls XDNN’s TVM registered function to access the FPGA runtime APIs© Copyright 2018 Xilinx Registering TVM op in Python at runtime File contrib_xlnx.py: … @tvm.register_func("tvm.accel.accel_fused") contains multiple stages, performance limited by slowest one ˃ Performance results based on Xilinx own runtime pipeline available in github (https://github.com/Xilinx/ml-suite/blob/master/examples/deployment_modes/mp_classify

dependent: arange, nms, etc. ○ Control flow: concatenate within a while loop Limitation of TVM/graph runtime ● Cannot compile and run dynamic models© 2019, Amazon Web Services, Inc. or its Affiliates. All TVM ● Support Any-dim in typing ● Use shape function to compute the type at runtime ● Virtual machine as a new runtime for Relay ● Dynamic codegen (WIP) ○ Kernel dispatch for a single op ○ Graph Fit for operator such as conv2d_NCHWc. Graph tuning is well defined for each subgraph. 3. Avoid runtime layout tracking system for operator requires layout transformation to optimize.© 2019, Amazon Web

站 Compile | libtvm_hexagon_runtime.so Alios TVM @ Hexagon DSP 。 Compute Kernel Offload to DSP ，loop nests marked as pipeline 。， Implement complete Hexagon runtime based on community PR. ADSPRPC inherits LLVM and 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

generate sql file with alter query statement for date-time and time field at location $\{ofbiz.home}/runtime/tempfiles/.sql You can use execute sql statement from any of the mysql batch command. 4.9.3. Changes can see test result web page by view runtime/logs/test- results/html/index.html file in web browser and see JUnit Test Result files for each test suite in runtime/logs/test-results directory. If you you use other target you only see JUnit Test Result file in runtime/logs/test-results. 10.2. Integration Tests 63 11. Deployment For a core configuration guide check the OFBiz configuration Guide (some