Implications of the Use of Magnetic Tunnel Junctions as Synapses in Neuromorphic Systems

Spin transfer torque magnetic random access memory (STT-MRAM) is a major breakthrough for embedded and standalone memory applications. Its basic cell, the magnetic tunnel junction, can also be used in a low-energy stochastic regime and implement a "synaptic" function. It can then be the basic element for learning-capable neuromorphic chips that do not separate logic and memory and exploit the magnetic tunnel junctions with an optimum energy efficiency. Implementing this vision, however, raises challenges at the circuit level. Proper addressing of the junctions can perturb their synaptic function. In this work, we investigate several architectures for a system based on stochastic synapses, and compare them in terms of reliability and energy efficiency. These results show the high potential of this technology, and pinpoint some main design challenges and tradeoff.