Efficient Table-based Function Approximation on FPGAs using Interval Splitting and BRAM Instantiation

TL;DR

This paper introduces an efficient FPGA-based method for function approximation using interval splitting and BRAMs, significantly reducing memory and resource usage while maintaining accuracy.

Contribution

It presents three novel interval-splitting algorithms, a low-latency hardware architecture, and automatic BRAM instantiation for memory-efficient function approximation on FPGAs.

Findings

Significant memory footprint reduction demonstrated on mathematical functions.

Hardware implementation achieves 9-cycle latency for sub-interval selection.

Automatic BRAM instantiation enhances resource efficiency.

Abstract

This paper proposes a novel approach for the generation of memory-efficient table-based function approximation circuits for FPGAs. Given a function f(x) to be approximated in a given interval [x0,x0+a] and a maximum approximation error Ea, the goal is to determine a function table implementation with a minimized memory footprint, i.e., number of entries that need to be stored. Rather than state-of-the-art work performing an even sampling of the given interval by so-called breakpoints and using linear interpolation between two adjacent breakpoints to determine f(x) at the maximum error bound, first, we propose three interval-splitting algorithms to reduce the required memory footprint drastically based on the observation that in sub-intervals of low gradient, a coarser sampling grid may be assumed to satisfy the maximum interpolation error bound. Experiments on elementary mathematical functions show that a large fraction in memory footprint may be saved. Second, a hardware architecture implementing the sub-interval selection, breakpoint lookup and interpolation at a latency of just 9 clock cycles is introduced. Third, within each generated circuit design, BRAMs are automatically instantiated rather than synthesizing the reduced footprint function table using LUT primitives providing an additional degree of resource efficiency.