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Neural processor development is reducing our reliance on remote server access to process deep learning operations in an increasingly edge-driven world. By employing inmemory processing, parallelization techniques, and algorithmhardware co-design, memristor crossbar arrays are known to efficiently compute large scale matrix-vector multiplications. However, state-of-the-art implementations of negative weights require duplicative column wires, and high precision weights using single-bit memristors further distributes computations. These constraints dramatically increase chip area and resistive losses, which lead to increased power consumption and reduced accuracy. In this paper, we develop an adaptive precision method by varying the number of memristors at each crosspoint. We also present a weight mapping algorithm designed for implementation on our crossbar array. This novel algorithm-hardware solution is described as the radix-X Convolutional Neural Network Crossbar Array, and demonstrate how to efficiently represent negative weights using a single column line, rather than double the number of additional columns. Using both simulation and experimental results, we verify that our radix-5 CNN array achieves a validation accuracy of 90.5% on the CIFAR-10 dataset, a 4.5% improvement over binarized neural networks whilst simultaneously reducing crossbar area by 46% over conventional arrays by removing the need for duplicate columns to represent signed weights.Index Terms-adaptive precision, algorithm hardware codesign, convolutional neural network (CNN), deep learning accelerator, low precision weight, memristor crossbar. I. INTRODUCTIONM ACHINE learning algorithms have become ubiquitous in the modern world, and are crucial in enabling computer systems which automatically update and improve with experience. This has opened up new frontiers in data analysis techniques. Deep learning refers to the use of a multilayered neural network where the sequence of layers between the input and output perform feature identification at various hierarchies, as inspired by an approximation of the neuronal connections within the brain [1]- [4]. A popular deep learning algorithm for structured data is the convolutional neural

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