Multi-state MRAM cells for hardware neuromorphic computing

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 x 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.

Automated summary

In this study, a complete hardware implementation design of a neural computing device that incorporates serially connected MTJs forming a multi-state memory cell is presented. The designed network shows a comparable detection ratio to the software algorithm in recognizing hand-written digits, using weights stored in a multi-cell consisting of four or more MTJs. Moreover, the presented solution has better energy efficiency in terms of energy consumed per single image processing, compared to a similar design.