Memristors Enabled Computing Correlation Parameter In-Memory System: A Potential Alternative to Von Neumann Architecture

• Published in: IEEE transactions on very large scale integration (VLSI) systems 2022-06, Vol.30 (6), p.755-768
• Main Authors: Kundu, Souvik, Ganganaik, Priyanka B., Louis, Jeffry, Chalamalasetty, Hemanth, Rao, BVVSN Prabhakar
• Format: Article
• Language: English
• Subjects: Arithmetic; Biometrics; Computation; Computational modeling; Computer architecture; Computer memory; Correlation; Correlation coefficients; Crossbar; Electrocardiography; Energy consumption; Medical research; memristor; Memristors; Microprocessors; Parameters; Pearson correlation coefficient (PCC); processing-in-memory (PIM); Task analysis
• ISSN: 1063-8210; 1557-9999
• DOI: 10.1109/TVLSI.2022.3161847

The von Neumann bottleneck has significantly increased the energy consumption of processing units and memory systems, especially in data-intensive computations such as the correlation parameter, which is being used in medical research, financial market analysis, biometrics, etc. Recently, memristor-enabled in-memory processing has gained tremendous research attraction to extenuate the von Neumann bottleneck as it processes operands at the location of storage, which obviates the need to transfer data between memory and the processing units. Hence, in this article, an innovative memristor crossbar-based architecture computing correlation parameter in-memory (CoCoPIM) has been proposed to accelerate correlation coefficient computations. Three different applications such as computing correlation between electrocardiogram (ECG) signals, faces, and H1N1 models were implemented based on the architecture. To evaluate the architecture, Neurosim was modified to support data mapping and computation steps, whereas Micro Architectural and System Simulator (MARSS) and multicore power, area, and timing (McPAT) were used to evaluate the von Neumann machine. In these applications, it was found that CoCoPIM was 41.04\times , 66.5\times , 67\times , and 33.2\times times energy-efficient against a four-core out-of-order processor in performing the respective tasks. It also achieved a speedup of 143.5\times , 52.5\times , 52.5\times , and 597\times times against the same von Neumann machine (multicore processor) for the respective tasks.