TVMQ@Alios AIOS ! 驱动万物智能 PRESENTATION AGENDA 人 人 e 人 e@ TVM Q@ AliOs Overview TVM @ AliOs ARM CPU TVM @ AliOos Hexagon DSP TVM @ Alios Intel GPU Misc /NiiOS ! 驱动万物智能 PART ONE TVM Q@ AliOs Overview Multimodal Interection CPU (ARM、Intel) 1驱动万物智能 Accelerated Op Library / Others Inference Engine DSP (Qualcomm) PART TWO Alios TVM @ ARM CPU AiOS 1驱动万物智能 Alios TVMQOARM CPU 。 Support TFLite ( Open Open Source and Upstream Master ) 。， Optimize on INT8 & FP32 AiiOS ! 驱动万物智能 Alios TVM @ ARM CPU INT8 * Cache 芍四 Data FO Data FOData … QNNPACK Convolution 。，NHWC layout Cach， 浆百

2019Bringing together the Arm ecosystemLinaro AI Initiative Provide the best-in-class Deep Learning performance by leveraging Neural Network acceleration in IP and SoCs from the Arm ecosystem, through collaborative Internal Jira project restricted to Linaro members ● Three sub-projects: ○ Arm Compute Library ○ Arm NN ○ Android NN Driver ● Arm Compute Library has been integrated by: ○ MATLAB Coder ○ ONNX RuntimeArm upstream IPs Target Hardware/Model Options Codegen CPU arm_cpu pixel2 (snapdragon 835), mate10/mate10pro (kirin 970), p20/p20pro (kirin 970) -target=arm64-linux-android -mattr=+neon llvm firefly rk3399

Target-independent Relay passes Target-optimized graph Target-dependent Relay passes Intel x86 ARM CPU Nvidia GPU ARM GPU Schedule templates written in TVM Tensor IR .. More targets AutoTVM – Tuning the Target-independent Relay passes Target-optimized Int8 Relay Graph Intel x86 schedule ARM CPU schedule Nvidia GPU schedule ARM GPU schedule Relay Int8 Graph Target-dependent Relay layout opt© 2019, Amazon Target-independent Relay passes Target-optimized Int8 Relay Graph Intel x86 schedule ARM CPU schedule Nvidia GPU schedule ARM GPU schedule Relay Int8 Graph Target-dependent Relay layout opt© 2019, Amazon

Labs 阿里巴巴人工智能实验室 AiILabs & TVM PART 1 : ARM32 CPU CONTENT PART 2 : HIFI4 DSP PART 3 : _ PowervVR GPU [和| Alibaba AL.Labs 阿里巴巴人工智能实验室 ARM 32 CPU Resolution Quantization Orize Kernel ALIOS ent pl 1=int8 int8 * int8 int32 = int16 1 + int16 x int8 Alibaba Al.Labs 阿里巴巴人工智能实验室 CPU : MTK8167S (ARM32 A35 1.5GHz) Model : MobileNetV2_ 1.0_ 224 400 336 350 3丈 300 250

import/export, and over 270 bugs fixed. 2.70 -- March 2014: Cycles gets basic volumetric support on the CPU, more improvements to the motion tracker, two new modeling modifiers, some UI consistency improvements GLSL which runs on the GPU for performance but falls back to the CPU for large images which might be slow when loaded with the GPU. Uses CPU for display transform and render images as a 2D texture. Fastest Cycles can use either the CPU or certain GPUs to render images, for more information see the GPU Rendering page. 無 When set to None or when the only option is None: the CPU will be used as the computing

import/export, and over 270 bugs fixed. 2.70 – March 2014: Cycles gets basic volumetric support on the CPU, more improvements to the motion tracker, two new modeling modifiers, some UI consistency improvements GLSL which runs on the GPU for performance but falls back to the CPU for large images which might be slow when loaded with the GPU. Uses CPU for display transform and render images as a 2D texture. Fastest Cycles can use either the CPU or certain GPUs to render images, for more information see the GPU Rendering page. None When set to None or when the only option is None: the CPU will be used as the computing

import/export, and over 270 bugs fixed. 2.70 -- March 2014: Cycles gets basic volumetric support on the CPU, more improvements to the motion tracker, two new modeling modifiers, some UI consistency improvements which runs on the GPU for performance but falls back to the CPU for large images which might be slow when loaded with the GPU. 2D Texture Uses CPU for display transform and render images as a 2D texture Cycles can use either the CPU or certain GPUs to render images, for more information see the GPU Rendering page. None When set to None or when the only option is None: the CPU will be used as the computing

import/export, and over 270 bugs fixed. 2.70 – March 2014: Cycles gets basic volumetric support on the CPU, more improvements to the motion tracker, two new modeling modifiers, some UI consistency improvements which runs on the GPU for performance but falls back to the CPU for large images which might be slow when loaded with the GPU. 2D Texture Uses CPU for display transform and render images as a 2D texture Cycles can use either the CPU or certain GPUs to render images, for more information see the GPU Rendering page. None When set to None or when the only option is None: the CPU will be used as the computing

the pop-up palette allows you to quickly access brushes, color history and a color selector within arm’s reach. The brushes are determined by tag, and pressing the lower-right configure button calls a drop-down Due to this vector data is much more editable, lighter, but at the same time it’s also much more CPU intensive. Operation Data Stuff like the filter layers, that tells Krita to change the colors of multithreading for the animation cache and handling the drawing of brush tips when using the pixel brush. CPU Limit The number of cores you want to allow Krita to use when multithreading. Frame Rendering Clones

import/export, and over 270 bugs fixed. 2.70 – March 2014: Cycles gets basic volumetric support on the CPU, more improvements to the motion tracker, two new modeling modifiers, some UI consistency improvements GLSL which runs on the GPU for performance but falls back to the CPU for large images which might be slow when loaded with the GPU. Uses CPU for display transform and render images as a 2D texture. Fastest Cycles can use either the CPU or certain GPUs to render images, for more information see the GPU Rendering page. None When set to None or when the only option is None: the CPU will be used as the computing