DIVE presents a comprehensive framework designed to facilitate the simulation of Deep Neural Network (DNN) workloads across various accelerators and systems, while offering a range of simulation accuracy levels. 

The framework can take Pytorch models as input, parse all the operators, and profile their performance on user-defined simulated accelerators. Users can choose anywhere from roofline models to analytical models to cycle-level simulators for the accelerator. DIVE is built on top of well-known simulators like MAESTRO and SCALE-SIM, leveraging their capabilities.

DIVE automates the generation of performance visualizations, presenting the results atop a roofline plot, and provides comprehensible metrics that enable users to gain insightful insights into the characteristics of the workload.

After the tutorial users can quickly simulate various configurations of workloads and systems. A few illustrative examples of these configurations encompass:

1. Executing Resnet50/Bert on Nvidia V100 hardware.

2. Employing a custom model on established hardware platforms like GPUs, TPUs, MAERI, or Eyeriss.

3. Simulating various DNN workloads (e.g., RN50, Alexnet, BERT, LLMs, etc.) on user-defined hardware setups.

4. Simulating custom workloads on hardware configurations defined by the user.

Additionally, users can explore the trade-offs between simulation and accuracy by simulating the chosen hardware configuration within a matter of seconds to a few minutes, contingent on the accuracy requirements of the simulation framework.