Approximate Computing Circuits for Embedded Tactile Data Processing

In this paper, we demonstrate the feasibility and efficiency of approximate computing techniques (ACTs) in the embedded Support Vector Machine (SVM) tensorial kernel circuit implementation in tactile sensing systems. Improving the performance of the embedded SVM in terms of power, area, and delay can be achieved by implementing approximate multipliers in the SVD. Singular Value Decomposition (SVD) is the main computational bottleneck of the tensorial kernel approach; since digital multipliers are extensively used in SVD implementation, we aim to optimize the implementation of the multiplier circuit. We present the implementation of the approximate SVD circuit based on the Approximate Baugh-Wooley (Approx-BW) multiplier. The approximate SVD achieves an energy consumption reduction of up to 16% at the cost of a Mean Relative Error decrease (MRE) of less than 5%. We assess the impact of the approximate SVD on the accuracy of the classification; showing that approximate SVD increases the Error rate (Err) within a range of one to eight percent. Besides, we propose a hybrid evaluation test approach that consists of implementing three different approximate SVD circuits having different numbers of approximated Least Significant Bits (LSBs). The results show that energy consumption is reduced by more than five percent with the same accuracy loss.

Automated summary

This paper demonstrates the feasibility and efficiency of approximate computing techniques (ACTs) in implementing embedded Support Vector Machine (SVM) tensorial kernel circuit in tactile sensing systems. By optimizing the implementation of the multiplier circuit in Singular Value Decomposition (SVD), the performance of embedded SVM in terms of power, area, and delay can be improved. The approximate SVD achieves a significant energy consumption reduction while maintaining a low Mean Relative Error (MRE). The study also evaluates the impact of approximate SVD on classification accuracy and proposes a hybrid evaluation test approach for further optimization.