Multi-state MRAM cells for hardware neuromorphic computing

• Published in: Scientific reports 2022-05, Vol.12 (1), p.7178-7178, Article 7178
• Main Authors: Rzeszut, Piotr, Chȩciński, Jakub, Brzozowski, Ireneusz, Ziȩtek, Sławomir, Skowroński, Witold, Stobiecki, Tomasz
• Format: Article
• Language: English
• Subjects: 639/166; 639/301; 639/705; 639/925; Algorithms; Computers; Design; Energy efficiency; Humanities and Social Sciences; Image processing; Information storage; Information Storage and Retrieval; multidisciplinary; Neural Networks, Computer; Neurons - physiology; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-11199-4

Magnetic tunnel junctions (MTJ) have been successfully applied in various sensing application and digital information storage technologies. Currently, a number of new potential applications of MTJs are being actively studied, including high-frequency electronics, energy harvesting or random number generators. Recently, MTJs have been also proposed in designs of new platforms for unconventional or bio-inspired computing. In the current work, we present a complete hardware implementation design of a neural computing device that incorporates serially connected MTJs forming a multi-state memory cell can be used in a hardware implementation of a neural computing device. The main purpose of the multi-cell is the formation of quantized weights in the network, which can be programmed using the proposed electronic circuit. Multi-cells are connected to a CMOS-based summing amplifier and a sigmoid function generator, forming an artificial neuron. The operation of the designed network is tested using a recognition of hand-written digits in 20 × 20 pixels matrix and shows detection ratio comparable to the software algorithm, using weights stored in a multi-cell consisting of four MTJs or more. Moreover, the presented solution has better energy efficiency in terms of energy consumed per single image processing, as compared to a similar design.