This repository contains datasets for training machine learning models that predict the thermal spatial distribution of chips based on their performance metrics. The data is collected on Intel i5-3337U CPU, Intel i7-8650U CPU, AMD Ryzen 7 4800U CPU, NVIDIA GeForce RTX 4060 GPU, and Google Coral M.2 TPU. We will continue to expend this database going forward. Please stay tuned...
The data is stored in serialized Python object format and can be read using Python's pickle module. Each file contains a Python dictionary with two items. "input" stores a 2D/3D Numpy array representing performance metrics or other inputs for the model prediction. "output" stores a 3D array representing the thermal map data. The first dimension indicates the data point number within the dataset. For each data point, the input can either be a vector (1D) or a time series (2D), while the output is a 2D thermal map (in degrees Celsius).
Due to GitHub's file size limitations, we only store some samples here. The complete datasets are available upon request.
Lu J, Tan S X, "Thermal Map Dataset for Commercial Multi/Many Core CPU/GPU/TPU", Proceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD, vol. 33, pp. 1-7, 2024. 10.1145/3670474.3685963. The paper can be download here Commerical Thermal Map Dataset.pdf, For citation: https://dl.acm.org/doi/10.1145/3670474.3685963
AI-Empowered Thermal Modeling and Run-Time Management for Manycore Processor and Chiplet Designs
Files starting with 'CPU_i5' and 'CPU_i7' contain data for the Intel i5-3337U and i7-8650U CPU. Each file corresponds to continuous recordings of CPU performance metrics and thermal maps over time under a specific task. For each data point, the input is a vector and the output is a heat map. You can stack the performance metrics data from the time points preceding a given time to form a time series for training a time series model.
Fig.1 Thermal map of Intel i5-3337U
Fig.2 Thermal map of Intel i7-8650U
Fig.3 i7-8650U with temperatures at sensor and true hot spot
CPU_R7_4800U.pkl contain data for the AMD Ryzen 7 4800U CPU. For each data point, the input is already a time series and the output is the corresponding thermal map.
AMD.Ryzen.7.4800U.HotSpots.Released.mov
The thermal map video showing the changing hot spots across different cores over time.
GPU_RTX_4060.pkl contain data for NVIDIA GeForce RTX 4060 GPU. For each data point, the input is already a time series and the output is the corresponding thermal map.
Fig.4 Thermal map of NVIDIA RTX 4060
TPU_Google_Edge.pkl contain data for the Google Coral M.2 TPU. Due to its task-specific nature, we do not use performance metrics to predict real-time temperature. Instead, we use the features of the machine learning tasks deployed on it to predict the steady-state temperature during runtime. For each data point, the input is a feature vector of the workload, and the output is the steady-state thermal map.
Fig.5 Thermal map of Google Coral M.2 TPU