Runtimes NPUModule CUDAModule TFModule tvm::runtime::Module GetFunction(string) -> tvm::runtime::PackedFunc SaveToBinary/LoadFromBinary Runtime Module Interface SubclassesUnified Runtime Benefit mod. = tvm.module.load("mylib.so") func = lib["npufunction0"] func(a, b) Automatic RPC Support remote = tvm.rpc.connect(board_url, port) remote.upload("mylib.so") remote_mod = remote.load_module(“mylib Single unified module/pass, type system, with function variants supportCompilation Flow under the New Infra IRModule (relay::Function) IRModule (te::Function, ExternFunc, …) runtime::Module High-level

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 contribute new infrastructure multiple employees to contribute to TVML. ee Today we'ltouch on a few of those contribution areas: o Core Infrastructure Improvements to TVM o_uTVM: support for microcontrollers in TVM o_ Virtual Machine dynamic NNs support (w/ AWS folks) o_ Improved NLP support, with focus on transformers QQ octoML Core Infrastructure Refactors ee New Integer Analysis Infrastructure o_ Supports the ability to handle

reserved. Option 2: Graph-Level Annotation ● Implement a Relay IR visitor to annotate a subgraph ● Module path: python/tvm/relay/op/contrib//graph_annotator.py ● Apply the annotator to its Affiliates. All rights reserved. Implement the Runtime Dispatcher ● Implement a TVM runtime module to dispatch the subgraph to the generated executable engine ● Runtime path: src/runtime/contri or its Affiliates. All rights reserved. Thank You and Q&A System Prototyping https://github.com/apache/incubator-tvm/pull/4258 RFC https://discuss.tvm.ai/t/bring-your-own-codegen-to-tvm/4501© 2019

2017), where each Transformer block consists of an attention module and a Feed-Forward Network (FFN). However, for both the attention module and the FFN, we design and employ innovative archi- tectures limits the maximum batch size and sequence length. 2.1.2. Low-Rank Key-Value Joint Compression The core of MLA is the low-rank joint compression for keys and values to reduce KV cache: c?? ? = ? ???h?

MobileNetv2 LaneNet 图TFLite1core 图TFLite4core 国QNNPACK 1core 四QNNPACK4core 四TVM1core 四TVM4core AiOS 1驱动万物智能 Alios TVM @ ARM CPU FP32 。，NHWC layout 。 For pointwise

Allocation – Linux Kernel Module DEPLOY VTA ON INTEL FPGA Setup Environment Variables Navigate to 3rdparty/cma and build kernel module Copy kernel module to DE10-Nano and Install Module CMA API Reference©2019 TVM with USE_VTA_FPGA flag ON Step 6: Copy the compiled TVM to the SDCard Step 7: Install kernel module cma.ko and run apps/vta_rpc/start_rpc_server.sh Step 8: Configure vta/config/de10nano_config.json

I think that the training of…$10 billion models, yeah, could start sometime in 2025. Around these core compute costs sit additional high-cost layers: research, data acquisition and hosting, and a mix inference acceleration. Google’s TPU (Tensor Processing Unit) and Amazon’s Trainium chips are now core components of their AI stacks. Amazon claims its Trainium2 chips offer 30-40% better price-performance supply chains. Taiwan continues to play a pivotal role in this dynamic. Despite American invention of core semiconductor technology like transistors and EUV lithography, it is Taiwan’s TSMC – the world’s

Sparse Transformers, etc - Reduce precision with int8/float16 - very helpful to maintain model in core-private L1 dcaches - Use rational approximations for transcendentals (exp, tanh, erf, etc) - very lines of Relay IR) - A few days of work - TVM sampling model running in 30us on single server CPU core - Beat hand-written, highly optimized baselines (https://github.com/mozilla/LPCNet) by ~40% - Bonus:

chatbots, single-turn LLMs, or sentiment classifiers—are not agents. More concretely, an agent possesses core characteristics that allow it to act reliably and consistently on behalf of a user: 01 It leverages building agents Agent design foundations In its most fundamental form, an agent consists of three core components: 01 Model The LLM powering the agent’s reasoning and decision-making 02 Tools External

recent call last): File “/Users/leeboonstra/Documents/test_folder/rename_files.py”, line 7, in <module> text = toUpperCase(prefix) NameError: name ‘toUpperCase’ is not defined Snippet 4. I broke the File "/ Users/leeboonstra/Documents/test_folder/rename_files.py", line 7, in <module> text = toUpperCase(prefix) NameError: name 'toUpperCase' is not defined Debug what's wrong and