The use of Field Programmable Gate Arrays (FPGAs) has become increasingly popular in recent years due to their ability to provide high performance and flexibility. FPGAs are a type of integrated circuit that can be programmed to perform specific tasks, allowing for the development of custom hardware solutions. As such, they are often used for applications such as embedded systems, digital signal processing, and image processing.
However, the development of FPGA-based solutions can be time-consuming and complex due to the need for manual design and optimization. To address this challenge, researchers have developed automated frameworks that can be used to explore approximate accelerators on FPGA architectures. These frameworks are designed to reduce the time and effort required to develop FPGA-based solutions by automating the design process.
The automated framework typically consists of three main components: a synthesis engine, an optimization engine, and a verification engine. The synthesis engine is responsible for generating a hardware description language (HDL) code from a high-level specification. This code is then passed to the optimization engine, which performs optimizations such as logic synthesis and technology mapping. Finally, the verification engine verifies the correctness of the generated HDL code.
Using an automated framework can significantly reduce the time and effort required to explore approximate accelerators on FPGA architectures. By automating the design process, it is possible to quickly generate optimized HDL code that can be used to implement an approximate accelerator on an FPGA. Furthermore, the use of an automated framework can help reduce design errors, as the verification engine can detect any errors in the generated HDL code.
In summary, the use of an automated framework can significantly reduce the time and effort required to explore approximate accelerators on FPGA architectures. By automating the design process, it is possible to quickly generate optimized HDL code that can be used to implement an approximate accelerator on an FPGA. Furthermore, the use of an automated framework can help reduce design errors, as the verification engine can detect any errors in the generated HDL code.
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- Source: Plato Data Intelligence: PlatoAiStream