platform: Intel/arm CPU, Nividia/arm GPU, VTA…5 �������������� 1. Get a .log file from the autotvm on Ubuntu 2. Use the .log from step1 on Windows to generate the .dll for deployment 3. For application options if options else [ “-shared”, “-fPIC”, “-m32”] b. python tensorflow_blur.py to get the .log c. Use the .log, with target=“llvm –mcpu=i686 –mtriple=i686-linux-gnu” then TVM_NDK_CC=“clang –m32” python

1 − ?, 1 + ? � ?? � − ?D?? � ??||??? ? �� , (32) D?? � ??||??? ? � = ??? ? (??|?) ??(??|?) − log ??? ? (??|?) ??(??|?) − 1, (33) where ? and ? are hyper-parameters; and ?? is the advantage, computed }.$$ Final Answer: The final answer is $-\frac{2}{3}$. I hope it is correct. Problem: Evaluate $\log_21$. Solution: Table 27 | An example of MATH. 45 PROMPT You are an expert Python programmer, and

exp_dispatcher) vmc = relay.backend.vm.VMCompiler() with tvm.autotvm.apply_graph_best("resnet50_v1_graph_opt.log"): vm = vmc.compile(mod, "llvm") vm.init(ctx) vm.load_params(params)

Units ~300MM+ Units ~1B+ Units / Users ~4B+ Units Tens of Billions of Units MM Units in Log Scale Technology Compounding = Numbers Behind The Momentum13 AI Technology Compounding = Numbers Estimated Training Cost of Frontier AI Models – 2016-2024, per Epoch AI & Stanford Training Cost, USD (Log Scale) Approx. +2,400x Right now, [AI model training costs] $100 million. There are models in