Exploring Approximate Accelerators Using Automated FPGA Architectural Frameworks

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The use of approximate accelerators is becoming increasingly popular in the field of embedded computing. Approximate accelerators are specialized hardware components that can be used to speed up computationally intensive tasks, such as image processing or machine learning algorithms. Automated FPGA architectural frameworks are a great way to explore the potential of approximate accelerators.

FPGA stands for Field Programmable Gate Array. It is a type of integrated circuit that can be programmed to perform specific tasks. FPGAs are used in a variety of applications, ranging from aerospace and automotive to consumer electronics and industrial automation.

Automated FPGA architectural frameworks provide a way to quickly and easily explore the potential of approximate accelerators. These frameworks allow users to quickly create and evaluate different architectures for their approximate accelerators. This can help reduce development time and cost, as well as improve the performance of the accelerator.

The automated FPGA architectural framework typically consists of several components. First, there is a synthesis tool that takes a high-level description of the approximate accelerator and generates a low-level implementation. This implementation is then fed into a place-and-route tool, which maps the design onto the FPGA. Finally, the optimization tool is used to refine the design and optimize it for the target application.

Using an automated FPGA architectural framework makes it easier to explore the potential of approximate accelerators. It allows users to quickly create and evaluate different architectures for their approximate accelerators, reducing development time and cost. Additionally, it can help improve the performance of the accelerator by optimizing it for the target application.

Overall, automated FPGA architectural frameworks are a great way to explore the potential of approximate accelerators. They provide users with a way to quickly create and evaluate different architectures for their approximate accelerators, reducing development time and cost while improving performance. With this technology, embedded computing applications can benefit from the use of approximate accelerators in ways that were not possible before.

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