This disclosure describes techniques to predict power consumption of a computing device under design. Per techniques of this disclosure, a machine learning model is trained based on parameterized hardware attributes of existing devices. A training dataset is obtained based on device parameters and power consumption of existing computing devices. The generated training dataset is utilized to train a machine learning (ML) model which is tested using a variety of hardware combinations. The ML model is verified using a test scenario and computing device configurations that were excluded from the training dataset. Upon successful verification, the ML model can be used for estimating power consumption of computing devices under design based on th...
In this paper, we present a flexible, simple and accurate power modeling technique that can be used ...
There exists a vast amount of possibilities of hardware platforms and models when implementing a mac...
This work focused on the power estimation of plug load devices, and in particular on Personal Comput...
Abstract — Reducing power consumption has become a priority in microprocessor design as more devices...
International audienceIn this paper, we present a new, simple, accurate and fast power estimation te...
In this article, we present a new, simple, accurate, and fast power estimation technique that can be...
International audienceWhen high speed and high performance are key features of a specific FPGA-based...
The purpose of this study is to make precise estimations of the amount of power consumed by CMOS VLS...
Energy and power are the main design constraints for modern high-performance computing systems. Inde...
Computing has undergone a significant transformation over the past two decades, shifting from a mach...
CPUs and dedicated accelerators (namely GPUs and FPGAs) continue to grow increasingly large and comp...
International audiencePower consumption has became a critical concern in modern computing systems fo...
International audienceMachine Learning (ML) is the process of developing Artificial Intelligence (AI...
In this paper, we present a flexible, simple and accurate power modeling technique that can be used ...
There exists a vast amount of possibilities of hardware platforms and models when implementing a mac...
This work focused on the power estimation of plug load devices, and in particular on Personal Comput...
Abstract — Reducing power consumption has become a priority in microprocessor design as more devices...
International audienceIn this paper, we present a new, simple, accurate and fast power estimation te...
In this article, we present a new, simple, accurate, and fast power estimation technique that can be...
International audienceWhen high speed and high performance are key features of a specific FPGA-based...
The purpose of this study is to make precise estimations of the amount of power consumed by CMOS VLS...
Energy and power are the main design constraints for modern high-performance computing systems. Inde...
Computing has undergone a significant transformation over the past two decades, shifting from a mach...
CPUs and dedicated accelerators (namely GPUs and FPGAs) continue to grow increasingly large and comp...
International audiencePower consumption has became a critical concern in modern computing systems fo...
International audienceMachine Learning (ML) is the process of developing Artificial Intelligence (AI...
In this paper, we present a flexible, simple and accurate power modeling technique that can be used ...
There exists a vast amount of possibilities of hardware platforms and models when implementing a mac...
This work focused on the power estimation of plug load devices, and in particular on Personal Comput...