Exploring Approximate Accelerators Using Automated Framework on FPGAs

Time Stamp: March 10, 2023 2:41 PM
Source Node: 2006317

The use of Field Programmable Gate Arrays (FPGAs) has become increasingly popular in recent years due to their ability to be reprogrammed for different tasks. This makes them ideal for applications that require high performance and flexibility, such as machine learning and artificial intelligence. One of the most promising areas of research in this field is the use of approximate accelerators on FPGAs. Approximate accelerators are specialized hardware components that can be used to speed up computations by sacrificing accuracy for speed.

The challenge with approximate accelerators is that they require a significant amount of manual effort to configure and optimize for a given application. This is where automated frameworks come in. Automated frameworks are software tools that can be used to quickly configure and optimize approximate accelerators on FPGAs. These frameworks provide a range of features, such as automated synthesis, design space exploration, and optimization.

One example of an automated framework for approximate accelerators on FPGAs is the Approximate Computing Toolbox (ACT). ACT is an open source toolbox developed by the University of California, Berkeley. It provides a range of features, such as automated synthesis, design space exploration, and optimization. It also includes a library of pre-defined approximate computing primitives that can be used to quickly configure approximate accelerators on FPGAs.

Another example of an automated framework for approximate accelerators on FPGAs is the Approximate Computing Library (ACL). ACL is an open source library developed by Intel. It provides a range of features, such as automated synthesis, design space exploration, and optimization. It also includes a library of pre-defined approximate computing primitives that can be used to quickly configure approximate accelerators on FPGAs.

The use of automated frameworks for approximate accelerators on FPGAs has the potential to significantly reduce the time and effort required to configure and optimize these components. This could lead to faster development cycles and more efficient designs. Additionally, these frameworks can be used to explore different design spaces and identify the best possible configuration for a given application.

In conclusion, automated frameworks are an invaluable tool for exploring and optimizing approximate accelerators on FPGAs. They can significantly reduce the time and effort required to configure and optimize these components, leading to faster development cycles and more efficient designs. Additionally, these frameworks can be used to explore different design spaces and identify the best possible configuration for a given application.

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