The use of Field Programmable Gate Arrays (FPGAs) has become increasingly popular in the modern computing world. This is due to their ability to be reconfigured to meet the specific needs of a given application. FPGAs are especially useful for applications that require high performance and low power consumption. However, the design process for FPGAs can be complex and time-consuming. To address this issue, researchers have developed an automated FPGA architecture-space exploration framework that can be used to explore approximate accelerators.
This framework is based on a combination of machine learning techniques and heuristic search algorithms. It is designed to automate the process of exploring the architecture-space of FPGAs. This means that it can identify the most suitable architecture for a given application, taking into account factors such as power consumption, performance, and cost. The framework also allows for the exploration of approximate accelerators, which are specialized hardware components designed to speed up specific operations.
The framework works by first generating a set of possible architectures for a given application. It then uses machine learning techniques to evaluate these architectures and identify the most suitable one. Finally, it uses heuristic search algorithms to explore the approximate accelerator space. This allows it to identify the best approximate accelerator for a given application.
The framework has been successfully used to explore approximate accelerators for various applications, including image processing, computer vision, and machine learning. It has also been used to optimize the performance and power consumption of FPGAs for various applications. Overall, this automated FPGA architecture-space exploration framework is an invaluable tool for exploring approximate accelerators and optimizing FPGA performance.
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