New memristor-based system from China boosts AI data sorting efficiency

Chinese researchers from Peking University and the Chinese Institute for Brain Research have developed a novel memristor-based hardware system that significantly enhances data sorting efficiency for AI and scientific computing applications. By integrating memristors—components capable of both memory and processing functions—with an advanced iterative search-based sorting algorithm, the system achieves a 7.7-fold increase in throughput and improves energy efficiency by over 160 times compared to conventional sorting methods. Additionally, it boosts area efficiency by more than 32 times, marking a major advancement toward combining storage and computation in a single platform. This innovation addresses the longstanding Von Neumann bottleneck, where traditional computing architectures separate memory and processing units, causing delays in data transfer and limiting performance. Unlike typical resistors, memristors retain memory of electrical charge flow, enabling them to perform computations directly within memory. The researchers’ approach eliminates the need for comparison operations common in traditional sorting algorithms by using memristors to iteratively identify minimum or maximum values, thereby reducing time and