Unlocking approximation for in-memory computing with Cartesian genetic programming and computer algebra for arithmetic circuits

Abstract

Abstract With ReRAM being a non-volative memory technology, which features low power consumption, high scalability and allows for in-memory computing, it is a promising candidate for future computer architectures. Approximate computing is a design paradigm, which aims at reducing the complexity of hardware by trading off accuracy for area and/or delay. In this article, we introduce approximate computing techniques to in-memory computing. We extend existing compilation techniques for the Programmable Logic in-Memory (PLiM) computer architecture, by adapting state-of-the-art approximate computing techniques for arithmetic circuits. We use Cartesian Genetic Programming for the generation of approximate circuits and evaluate them using a Symbolic Computer Algebra-based technique with respect to error-metrics. In our experiments, we show that we can outperform state-of-the-art handcrafted approximate adder designs.