Energy Efficient Knapsack Optimization Using Probabilistic Memristor Crossbars

TL;DR

This paper introduces a novel probabilistic memristor-based hardware solution for energy-efficient knapsack optimization, outperforming digital and quantum methods by over 10,000 times in energy efficiency, suitable for real-world, dense, non-binary problems.

Contribution

The paper presents the first implementation of a probabilistic memristor crossbar for knapsack optimization, demonstrating significant energy efficiency improvements over existing approaches.

Findings

Outperforms digital and quantum approaches by over 4 orders of magnitude in energy efficiency.

Successfully implements a randomized Ising-inspired algorithm on analog memristor hardware.

Handles dense, non-binary, and destabilizing problem representations effectively.

Abstract

Constrained optimization underlies crucial societal problems (for instance, stock trading and bandwidth allocation), but is often computationally hard (complexity grows exponentially with problem size). The big-data era urgently demands low-latency and low-energy optimization at the edge, which cannot be handled by digital processors due to their non-parallel von Neumann architecture. Recent efforts using massively parallel hardware (such as memristor crossbars and quantum processors) employing annealing algorithms, while promising, have handled relatively easy and stable problems with sparse or binary representations (such as the max-cut or traveling salesman problems).However, most real-world applications embody three features, which are encoded in the knapsack problem, and cannot be handled by annealing algorithms - dense and non-binary representations, with destabilizing self-feedback. Here we demonstrate a post-digital-hardware-friendly randomized competitive Ising-inspired (RaCI) algorithm performing knapsack optimization, experimentally implemented on a foundry-manufactured CMOS-integrated probabilistic analog memristor crossbar. Our solution outperforms digital and quantum approaches by over 4 orders of magnitude in energy efficiency.