datapoints turned into this beast. As soon as we updated one chart, we often had to update another – a data game of whack-a-mole… a pattern that shows no sign of stopping…and will grow more complex as competition related to the artificial intelligence technology evolution is indeed unprecedented, as supported by the data. This document is filled with user, usage and revenue charts that go up-and-to-the-right… often supported Unprecedented • AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise • AI & Physical World Ramps = Fast + Data-Driven • Global Internet User Ramps Powered by AI from Get-Go =

maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models. The model checkpoints are available at h t t p s : / / g i t h u b . c o m / d e e p s e (Tokens/Sec) (b) Figure 1 | (a) MMLU accuracy vs. activated parameters, among different open-source models. (b) Training costs and inference efficiency of DeepSeek 67B (Dense) and DeepSeek-V2. Contents

Inference engines DL Compilers Kenrel Libraries Hardware CuDNN NNPack MKL-DNN Hand optimized Open source, automated end-to- end optimization framework for deep learning.TVM Stack High-Level Differentiable Cloud FPGA ASIC 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 intensiveMachine Learning based Program Optimizer TVM: Learning-based Learning System High-level data flow graph and optimizations Directly generate optimized program for new operator workloads and

as image prompts) is the input the model uses to predict a specific output. You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. However, crafting the most complicated. Many aspects of your prompt affect its efficacy: the model you use, the model’s training data, the model configurations, your word-choice, style and tone, structure, and context all matter. Therefore responses, and can hinder the model’s ability to provide meaningful output. You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. Prompt Engineering February

Q OctoML Open Source at O〇ctoML TVM Meetup 11/8/2019 Jared Roesch OctoML is a new company building DL deployment solutions using the Apache (incubating) TVM project. A goal is to nurture the TVM community and machine learning tr tvm 。 @zxnet 和os 全 W Open Source at OctoML ee We are big believers in the power of open source o 5S$ponsoring multiple employees to contribute to TVML. ee Today implementation httpsJigithub,comlapachelincubator-tvmipull4274 remumn dming data AutoTYM 二 QQ octoML Coming Soon to HTVM (Self-Hosted Models) Host

Instruction Buffer Cross Bar Pooling/ EWA© Copyright 2018 Xilinx Xilinx Edge DPU IP (DPUv2) Source: Published results from Huawei 18% 13% 14% 40% 24% 23% 85% 51% 52% 0% 20% 40% 60% 80% 100% VGG16 ResNet-50 GoogleNet-V3 Aristotle on 7020 FPGA Iphone8plus Kirin 970 CPU MEM CONTROLLER BUS Data Mover IMG WR SCHEDULER WEIGHTS WR SCHEDULER SMART MEM FABRIC IMG RD SCHEDULER WEIGHTS RD node in TVM graph { "nodes": [ { "op": "null", "name": "data", "inputs": [] }, { "op": "tvm_op", "name": "xdnn0", "attrs": { "flatten_data": "0", "func_name": “accel_fused", "num_inputs": "1", "num_outputs":

Quantize Operator fn (%input_data: Tensor[(2, 5), float32]) { qnn.quantize(%input_data, out_dtype="uint8", output_zero_point=127, output_scale=0.5f) } def @main(%input_data: Tensor[(2, 5), float32]) -> -> Tensor[(2, 5), uint8] { %0 = divide(%input_data, 0.5f /* ty=float32 */) /* ty=Tensor[(2, 5), float32] */; %1 = round(%0) /* ty=Tensor[(2, 5), float32] */; %2 = cast(%1, dtype="int32") /* ty=Tensor[(2 conv2d fn (%data: Tensor[(1, 3, 2, 3), uint8], %weight: Tensor[(3, 3, 2, 2), uint8]) { qnn.conv2d(%data, %weight, … , out_dtype="int32", input_zero_point=1, kernel_zero_point=1)} def @main(%data: Tensor[(1

employees can focus on 
 the things only people can do. And because AI can process huge amounts of data from many sources, it can create customer experiences that feel more human because they’re more relevant need to explain to the candidate why this specific job was recommended to them. Indeed uses the data analysis and natural language capabilities of GPT-4o mini to shape these ‘why’ statements in their function. With thousands of suppliers, Lowe’s often has to work with incomplete or inconsistent product data. 13 AI in the EnterpriseThe key is in accurate product descriptions and tagging. But it also requires

TVMQOARM CPU 。 Support TFLite ( 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 re 。 Tensorize GEMM Cache 大站 Fe Data FO Data … FOData QNNPACK /NiiOS ! 驱动万物智能 P Cache 浆加 Data FO Data FOData … NHWC L2 da … FL2 da Alios TVM @ ARM CPU INT8 TVM /QNNPACK

collaborative seamless integration with the ecosystem of AI/ML software frameworks and librariesArm NN open source project ● Linaro-hosted https://www.mlplatform.org/ ● Git and review servers ● Forums and issue