  Biologically-inspired computing models have made significant progress in
recent years, but the conventional von Neumann architecture is inefficient for
the large-scale matrix operations and massive parallelism required by these
models. This paper presents Spin-NeuroMem, a low-power circuit design of
Hopfield network for the function of associative memory. Spin-NeuroMem is
equipped with energy-efficient spintronic synapses which utilize magnetic
tunnel junctions (MTJs) to store weight matrices of multiple associative
memories. The proposed synapse design achieves as low as 17.4% power
consumption compared to the state-of-the-art synapse designs. Spin-NeuroMem
also encompasses a novel voltage converter with 60% less transistor usage for
effective Hopfield network computation. In addition, we propose an associative
memory simulator for the first time, which achieves a 5.05Mx speedup with a
comparable associative memory effect. By harnessing the potential of spintronic
devices, this work sheds light on the development of energy-efficient and
scalable neuromorphic computing systems. The source code will be publicly
available after the manuscript is reviewed.
