Fast and Scalable Memristive In-Memory Sorting with Column-Skipping Algorithm

TL;DR

This paper introduces a column-skipping algorithm for memristive in-memory sorting that significantly improves speed, area, and energy efficiency by reducing redundant memory reads and enhancing scalability across multiple memory banks.

Contribution

It proposes a novel column-skipping algorithm with near-memory circuitry and multi-bank management, advancing memristive in-memory sorting efficiency and scalability.

Findings

Achieves up to 4.08x speedup over existing methods

Improves area efficiency by 3.14x

Enhances energy efficiency by 3.39x

Abstract

Memristive in-memory sorting has been proposed recently to improve hardware sorting efficiency. Using iterative in-memory min computations, data movements between memory and external processing units can be eliminated for improved latency and energy efficiency. However, the bit-traversal algorithm to search the min requires a large number of column reads on memristive memory. In this work, we propose a column-skipping algorithm with help of a near-memory circuit. Redundant column reads can be skipped based on recorded states for improved latency and hardware efficiency. To enhance the scalability, we develop a multi-bank management that enables column-skipping for dataset stored in different memristive memory banks. Prototype column-skipping sorters are implemented with a 1T1R memristive memory in 40nm CMOS technology. Experimented on a variety of sorting datasets, the length-1024 32-bit column-skipping sorter with state recording of 2 demonstrates up to 4.08x speedup, 3.14x area efficiency and 3.39x energy efficiency, respectively, over the latest memristive in-memory sorting.