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 and dynamic NNs support Transformer Improvements Transformer based models such as BERT have recently become very Popular and require first class support in TVML. ee What we've done: o Extend the relay ONNX frontend to support many reshape operations, which are currently implemented using copy, 10 Virtual Machine e Many improvements from contributors at UW, AWS, and OctoML. e Initial implementation is quickly moving towards

semi-borderless capital…all driving massive change. Sport provides a good analogy for AI’s constant improvements. As athletes continue to wow us and break records, their talent is increasingly enhanced by better Breakthroughs in large models, cost-per-token declines, open-source proliferation and chip performance improvements are making new tech advances increasingly more powerful, accessible, and economically viable algorithms, based on how much computing power you'd need to reach top performance without any improvements. Source: Epoch AI (3/24) Impact of Improved Algorithms on AI Model Performance – 2014-2023, per

Quantizer Compiler Tensor Graph Optimization Framework Tensor Graph to Xilinx Tensor Graph Frontend Deep Learning Frameworks https://github.com/xilinx© Copyright 2018 Xilinx TVM as Unified ML Front

int32] */} Asymmetric© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Frontend Parsers • TFLite Pre-quantized Models • In good shape • Supports all Image Classification PreQuantized

224] dtype = "float32" block = get_model('resnet50_v1', pretrained=True) mod, params = relay.frontend.from_mxnet(block, shape={input_name: input_shape}, dtype=dtype) tvm.relay.transform.dispatch_global_func(mod

complex, interconnected workflows and systems. We’re seeing AI deliver significant, measurable improvements on three fronts: 01 Workforce performance Helping people deliver higher-quality outputs in shorter deployment to learn quickly from customer use cases and use that information to accelerate product improvements. That means shipping updates regularly, getting feedback, and improving performance and safety through iteration. The earlier you start, the more your organization benefits from compounding improvements. Klarna, a global payments network and shopping platform, introduced a new AI assistant to

a success message print("Files renamed successfully.") ``` Additionally, there are a few other improvements that can be made to the code: 1. The file extension of the new filenames is not kept. It’s better the file {file}: {e}") # Print a success message print("Files renamed successfully.") ``` These improvements make the code more robust and flexible while also making it easier to read and understand...

DeepSeek-V2 Chat (RL) on standard benchmarks. Notably, DeepSeek-V2 Chat (SFT) demon- strates substantial improvements in GSM8K, MATH, and HumanEval evaluations compared with its base version. This progress can be