reserved. TVM Overview Framework Graph Mxnet TF …. parsers Relay Graph Target-independent Relay passes Target-optimized graph Target-dependent Relay passes Intel x86 ARM CPU Nvidia GPU ARM GPU Dialect QNN passes 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 Dialect QNN passes 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

Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) Your Dispatcher Target Device General Devices (CPU/GPU/FPGA) Mark supported operators or subgraphs 1. Implement an operator-level WholeGraphAnnotator(ExprMutator): def __init__(self, target): super(WholeGraphAnnotator, self).__init__() self.target = target self.last_call = True def visit_call(self, if isinstance(param, relay.expr.Var): . param = subgraph_begin(param, self.target) params.append(param) new_call = relay.Call(call.op, params, call.attrs)

platform support in TVM 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 -mattr=+neon llvm firefly rk3399, rock960, ultra96 -target=aarch64-linux-gnu -mattr=+neon rasp3b (bcm2837) -target=armv7l-linux-gnueabihf -mattr=+neon pynq -target=armv7a-linux-eabi -mattr=+neon GPU mali (midgard)

Packed Func 0 Packed Func 1 ... Packed Func M Relay VM Executor exe = relay.vm.compile(mod, target) vm = relay.vm.VirtualMachine(exe) vm.init(ctx) vm.invoke("main", *args) export© 2019, Amazon register a strategy? @conv2d_strategy.register("cpu") def conv2d_strategy_cpu(attrs, inputs, out_type, target): strategy = OpStrategy() layout = attrs.data_layout if layout == "NCHW": oc, ic

its MoE-related communication frequency is proportional to the number of devices covered by its target experts. Due to the fine-grained expert segmentation in DeepSeekMoE, the number of activated experts DeepSeek-V2, beyond the naive top-K selection of routed experts, we additionally ensure that the target experts of each token will be distributed on at most ? devices. To be specific, for each token, we for carrying RoPE (Su et al., 2024). For YaRN, we set the scale ? to 40, ? to 1, ? to 32, and the target maximum context length to 160K. Under these settings, we can expect the model to respond well for

[ “-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 tf_blur.py��������

400us sampling net runtime Image from LPCNetExit, Pursued By A Bear - 3400us (baseline), 40us (target) - 85x speedup - Uh ohEnter, TVM and model co-design - PyTorch operator overhead makes interpreter

access from other languages QQ octoML HTVM Overview *。 Plug directly into TVYM as a backend *，Target C to emit code for microcontrollers that is device- agnostic AuroTYM QQ octoML AutoTVM on HTVM

© Copyright 2018 Xilinx Elliott Delaye FPGA CNN Accelerator and TVM© Copyright 2018 Xilinx TVM Target devices and models >> 2 HW Platforms ZCU102 ZCU104 Ultra96 PYNQ Face detection Pose estimation

are simple: 01 Set up evals to establish a performance baseline 02 Focus on meeting your accuracy target with the best models available 03 Optimize for cost and latency by replacing larger models with smaller