Pre-Clinical Candidate Status marks the point at which a lead molecule (or biologic) has satisfied all discovery-stage gates and is officially handed off to the development organization for work related to beginning ‘Insilico Medicine Reports Benchmarks for its AI-Designed Therapeutics’ (2/25) AI-Driven Drug Discovery – 2021-2024, Per Insilico Medicine, Cradle & BioPharmaTrend Months to Pre-Clinical Candidate Status Digital Trends (1/25) Get up to speed on just about anything with Deep Research, an agentic feature in Gemini that can automatically browse up to hundreds of websites on your behalf, think through

statements in their emails and messages to jobseekers. Using AI, the popular ‘Invite to Apply’ feature also explains why a candidate’s background or previous work experience makes the job 
 a good fit help guide your own thinking. Product Note: Operator Operator is an example of OpenAI’s agentic approach. Leveraging its own virtual browser, Operator can navigate the web, click on buttons, fill that previously required human intervention, such as: Automating software testing and QA using Operator to interact with web apps 
 like a real user, flagging any UI issues. Updating systems of record

function ● Relax type inference/checking for Any at compilation time ● Register a shape function for operator to check the type and compute the output shape© 2019, Amazon Web Services, Inc. or its Affiliates function ● Relax type inference/checking for Any at compilation time ● Register a shape function for operator to check the type and compute the output shape ● Shape function has two modes (op_attrs, input_tensors function ● Relax type inference/checking for Any at compilation time ● Register a shape function for operator to check the type and compute the output shape ● Shape function has two modes (op_attrs, input_tensors

FlashAttention-2 (Dao, 2023). We conduct all experiments on a cluster equipped with NVIDIA H800 GPUs. Each node in the H800 cluster contains 8 GPUs connected using NVLink and NVSwitch within nodes. Across nodes Although training an MoE model will introduce additional commu- nication overheads, through our operator and communication optimizations, the training for DeepSeek-V2 can attain a relatively high Model prompt and generation length distribution from the actually deployed DeepSeek 67B service. On a single node with 8 H800 GPUs, DeepSeek-V2 achieves a generation throughput exceeding 50K tokens per second, which

1980s and beyond. 2. **Blast From The Past: Iconic Arcade Games of The 80’s:** - This article would feature a glimpse into the history, impact, and legacy of some of the most iconic and influential arcade `f` string syntax for string interpolation is more readable and concise than the traditional `+` operator. 4. The code doesn’t handle errors that might occur during the renaming process. It would be better

subgraphs 1. Implement an operator-level annotator, OR 2. Implement a graph-level annotator© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Option 1: Operator-Level Annotation ● Implement Boolean functions in the template def conv2d(attrs, args): return is_float32(args) Relay operator name Operator attributes and args (inputs) can be checked as well Return True/False for this op After Device General Devices (CPU/GPU/FPGA) Mark supported operators or subgraphs 1. Implement extern operator functions, OR 2. Implement a graph annotator© 2019, Amazon Web Services, Inc. or its Affiliates

scratch • New Relay passes and TVM schedules required • AlterOpLayout, Graph Fusion etc require work/operator • No reuse of existing Relay and TVM infrastructure. Option 2 – Lower to a sequence of existing 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Lowering of QNN Quantize Operator fn (%input_data: Tensor[(2, 5), float32]) { qnn.quantize(%input_data, out_dtype="uint8", output_zero_point=127 Affiliates. All rights reserved. QNN Conv2D Operator • Calculations are different from FP32 Conv2D https://discuss.tvm.ai/t/tf-lite-quantized-conv2d-operator-conversion/2651/8 𝑟𝑒𝑎𝑙_𝑣𝑎𝑙𝑢𝑒 = 𝒔𝒄𝒂𝒍𝒆

cuDNN Offload to heavily optimized DNN operator library FrameworksLimitations of Existing Approach cuDNN Frameworks New operator introduced by operator fusion optimization potential benefit: System High-level data flow graph and optimizations Directly generate optimized program for new operator workloads and hardware Hardware FrameworksWhy Automation is the Future Clear winner on

3400us (baseline), 40us (target) - 85x speedup - Uh ohEnter, TVM and model co-design - PyTorch operator overhead makes interpreter infeasible - Reduce FLOPs with block-sparsified weight matrices -

for different integer division modes, floor division and truncating division. e Unified Object and Node system for TVM runtime o Lays groundwork forimproved multi-language support for expPosing runtime