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 on Ubuntu 2. Use the .log

(bcm2837) -target=armv7l-linux-gnueabihf -mattr=+neon pynq -target=armv7a-linux-eabi -mattr=+neon GPU mali (midgard) firefly rk3399, rock960 (mali t860) N/A opencl bifrost hikey960 (mali g71) N/A FPGA closely in an organized way ○ Arm - Cortex-A/Cortex-M/Neoverse CPU, Mali GPU, Ethos NPU ○ Qualcomm - Hexagon DSP, Adreno GPU ○ Hisilicon, Xilinx, NXP, TI, ST, Fujitsu, Riken, and etc ● Collaborations

strategy func GPU strategy func OpStrategy OpStrategy OpStrategy Default implement Specialized implement 1 Specialized implement 2 (e.g., winograd) kernel_size <= 3 b < 8 “cpu” “gpu”© 2019, Amazon

level: the less the better 。 The requirement of familiarity with WMMA API “Unified matmul schedule for GPU 。 Maintainability & Common Optimization Sharing 。 Search across the entire space (TensorCore + non-TensorCore)

from UW, Berkeley, Cornell, UCLA, Amazon, Huawei, NTT, Facebook, Microsoft, Qualcomm, Alibaba, Intel, … Incubated as Apache TVM recently. Independent governance, allowing competitors to collaborate