AbstractArtificial Intelligence (AI) is the domain of large resource intensive data centers that limit access to a small community of developers [1-3]. Neuromorphic hardware promises dramatically improved space and energy efficiency for AI, but is presently only capable of low-accuracy operations, like inferencing in neural networks [4-6]. Core computing tasks of signal processing, neural network training and natural language processing demand far higher computing resolution, beyond that of existing neuromorphic circuit elements [7-9]. Here we introduce an analogue molecular memristor based on a Ru-complex of an azo-aromatic ligand with 14-bit resolution. The supramolecular electronic structure of the film enables voltage-driven ionic rearrangement facilitating 16,520 distinct analogue levels. These levels can be linearly and symmetrically updated or written individually in one time-step, substantially simplifying the weight update procedure over existing neuromorphic platforms [4]. The circuit elements are unidirectional, facilitating a selector-less 6464 crossbar-based dot-product engine that enables vector-matrix multiplication, including Fourier transform, in a single time-step. We achieved >73 dB signal-to-noise-ratio, a four-order of magnitude improvement over the state-of-the-art [10-12], while consuming 460 less energy than digital computers [13]. Accelerators leveraging these molecular crossbars could transform neuromorphic computing, extending it beyond niche applications and reinforcing the core of digital electronics from the cloud to the edge [14,15].
