to computing, calculating or counting patents. Google patents data changes somewhat between each query so numbers are rounded and should be viewed as directionally accurate. Source: USA Patent & Trademark magnitude more compute. At scale, inference becomes a persistent cost center – one that grows in parallel with usage, despite declines in unit inference costs. The broader dynamic is clear: lower per-unit became the default engine for training and inference, prized for their ability to handle highly parallel computations at scale. Its proprietary technology – and unparalleled scale – has led to industry

approaches have been explored to address this issue, including Grouped-Query Attention (GQA) (Ainslie et al., 2023) and Multi-Query Attention (MQA) (Shazeer, 2019). However, these methods often compromise limit the inference efficiency. In order to reduce the KV cache, Multi-Query Atten- tion (MQA) (Shazeer, 2019) and Grouped-Query Attention (GQA) (Ainslie et al., 2023) are proposed. They require a smaller respectively: q? = ??h?, (1) k? = ? ?h?, (2) v? = ??h?, (3) 6 Grouped-Query Attention (GQA) Multi-Head Attention (MHA) Multi-Query Attention (MQA) Multi-Head Latent Attention (MLA) Keys Queries Values

module_pass(opt_level=4) class AccelModule:© Copyright 2018 Xilinx TVM Partitioning >> 7 Subgraph 1 Parallel Subgraphs Post-Processing Pre-Processing FPGA or CPU FPGA CPU CPU FPGA - More than supported/not Partitioning/Fusion >> 8 Subgraph 1 Parallel Subgraphs Post-Processing Pre-Processing CPU FPGA CPU CPU FPGA© Copyright 2018 Xilinx TVM Code Generation >> 9 Subgraph 1 Parallel Subgraphs Post-Processing Pre-Processing Pre-Processing CPU FPGA CPU CPU FPGA Parallel Subgraphs© Copyright 2018 Xilinx Registering external accelerator function @reg.register_compute("accel", level=15) def compute_accel(attrs,inputs,outputs):

generate HVX instruction 。， Add one Hexagon runtimes named as libtvm_hexagon_runtime.so to support parallel. 。 Could run end-to-end TFLite Mobilenet V2 quantized model on Simulator / Device. /NiiOS ! 驱动万物智能

Examples Data Enable agents to retrieve context and information necessary for executing the workflow. Query transaction databases or systems like CRMs, read PDF documents, or search the web. Action Enable

specific aspects of the RAG system that impact what content was inserted into the prompt, including the query, chunk settings, chunk output, and other information. Once you feel the prompt is close to perfect