DALTA: A Decomposition-based Approximate Lookup Table Architecture

Abstract

A popular way to implement an arithmetic function is through a lookup table (LUT), which stores the pre-computed outputs for all the inputs. However, its size grows exponentially with the number of input bits. In this work, targeting at computing kernels of error-tolerant applications, we propose DALTA, a reconfigurable decomposition-based approximate lookup table architecture, to approximately implement those kernels with dramatically reduced size. We also propose integer linear programming-based approximate decomposition methods to map a given function to the architecture. Our architecture features with low energy consumption and high speed. The experimental results show that our architecture achieves energy and latency savings by 56.5% and 92.4%, respectively, over the state-of-the-art approximate LUT architecture.