High-accuracy Low-power Reconfigurable Architectures for Decomposition-based Approximate Lookup Table

Storing pre-computed results of frequently-used functions into lookup table (LUT) is a popular way to improve energy efficiency, but its advantage diminishes as the number of input bits increases. A recent work shows that by decomposing the target function approximately, the total LUT entries can be dramatically reduced, leading to significant energy saving. However, its heuristic approximate decomposition algorithm leads to sub-optimal approximation quality. Also, its rigid hardware architecture only supports disjoint decomposition and may have unnecessary extra power consumption sometimes. To address these issues, we develop a novel approximate decomposition algorithm based on beam search and simulated annealing, which can reduce 11.1% approximation error. We also propose a non-disjoint approximate decomposition method and two reconfigurable architectures. The first has 10.4% less error using 19.2% less energy and the second has 23.0% less error with same energy consumption compared to the state-of-the-art design.