ChenCurrent Deep Learning Landscape Frameworks and Inference engines DL Compilers Kenrel Libraries Hardware CuDNN NNPack MKL-DNN Hand optimized Open source, automated end-to- end optimization framework Optimization AutoTVM Device FleetExisting Deep Learning Frameworks High-level data flow graph Hardware Primitive Tensor operators such as Conv2D eg. cuDNN Offload to heavily optimized DNN operator graph and optimizations Directly generate optimized program for new operator workloads and hardware Hardware FrameworksWhy Automation is the Future Clear winner on emerging models in product Competitive

been integrated by: ○ MATLAB Coder ○ ONNX RuntimeArm platform support in TVM upstream IPs Target Hardware/Model Options Codegen CPU arm_cpu pixel2 (snapdragon 835), mate10/mate10pro (kirin 970), p20/p20pro runtime plugins? ○ Integrate TVM codegen into Arm NN? ● CI and benchmark testing for TVM on member hardware platforms ○ Shall we maintain a list of Arm platforms supported by TVM? More details from our

relay.vm.compile Relay Object (hardware independent) Code segment VM Func 0 VM Func 1 ... VM Func N Data segment Const 0 Const 1 ... Const K Kernel lib (hardware dependent) Packed Func 0 Packed

our network, but also get a good performance gain by autotuning 3. TVM can support many kinds of hardware platform: Intel/arm CPU, Nividia/arm GPU, VTA…5 �������������� 1. Get a .log file from the autotvm