Automated Circuit Approximation Method Driven by Data Distribution

Abstract

We propose an application-tailored data-driven fully automated method for functional approximation of combinational circuits. We demonstrate how an application-level error metric such as the classification accuracy can be translated to a component-level error metric needed for an efficient and fast search in the space of approximate low-level components that are used in the application. This is possible by employing a weighted mean error distance (WMED) metric for steering the circuit approximation process which is conducted by means of genetic programming. WMED introduces a set of weights (calculated from the data distribution measured on a selected signal in a given application) determining the importance of each input vector for the approximation process. The method is evaluated using synthetic benchmarks and application-specific approximate MAC (multiply-and-accumulate) units that are designed to provide the best trade-offs between the classification accuracy and power consumption of two image classifiers based on neural networks.

Accepted for publication at Design, Automation and Test in Europe (DATE 2019). Florence, Italy

Cited by in corpus (4)

• Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey
• Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications
• TransAxx: Efficient Transformers with Approximate Computing
• Evolutionary Algorithms in Approximate Computing: A Survey