on Page 332 27% 19% 8/23 4/25 % of San Francisco Gross Bookings 34% 0% East Asia & Pacific Sub-Saharan Africa South Asia North America Middle East & North Africa Latin America & Caribbean Europe next layers of AI infrastructure: agentic interfaces, enterprise copilots, real-world autonomous systems, and sovereign models. Rapid advances in artificial intelligence, compute infrastructure, and global implications are just starting to emerge. AI agents could reshape how users interact with digital systems – from customer support and onboarding to research, scheduling, and internal operations. Enterprises

Advances in reasoning, multimodality, and tool use have unlocked a new category of LLM-powered systems known as agents. This guide is designed for product and engineering teams exploring how to build to perform the same workflows on the users’ behalf with a high degree of independence. Agents are systems that independently accomplish tasks on your behalf. A workflow is a sequence of steps that must be and transfer control back to the user. 02 It has access to various tools to interact with external systems—both to gather context and to take actions—and dynamically selects the appropriate tools depending

will increasingly do their best work with sophisticated, complex, interconnected workflows and systems. We’re seeing AI deliver significant, measurable improvements on three fronts: 01 Workforce performance our own work. An example: Our support teams were getting bogged down, spending time accessing systems, trying to understand context, craft responses, and take the right actions for customers. So we we built an internal automation platform. It works on top of our existing workflows and systems to automate rote work and accelerate insight and action. Our first use case: working on top of Gmail to

model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023. G. Lai, Q. Xie, H. Liu, Y. Yang, and E. H. Hovy. RACE: large-scale reading comprehension language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730–27744, 2022. 24 B. Peng, J. Quesnelle, H. Fan, and E. Shippole. Yarn: Efficient context mixture of experts. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, pages 8583–8595, 2021. URL https://proceedings

Nenana Intel orMicrosof Apple Qualcomm 40+ years of combined experience in computer systems design and machine learning tr tvm 。 @zxnet 和os 全 W Open Source at OctoML ee We

Subsystem TPUsTensorization Challenge Compute primitives scalar vector tensor Challenge: Build systems to support emerging tensor instructionsTensorization Challenge C = tvm.compute((m, n), 


Public mailing lists and IRC channel ● Internal Jira project restricted to Linaro members ● Three sub-projects: ○ Arm Compute Library ○ Arm NN ○ Android NN Driver ● Arm Compute Library has been integrated

Tensorization 下 CUDA CodeGen *。IR passes to automatically transform sub-tree to TensorCore Intrinsics Pattern Matching 计算平台事业部 shared/global

runtime for Relay ● Dynamic codegen (WIP) ○ Kernel dispatch for a single op ○ Graph dispatch for a (sub-)graph In collaboration with Jared Roesch, Zhi Chen, Wei Chen© 2019, Amazon Web Services, Inc. or