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The current trend for deep learning has come with an enormous computational need for billions of Multiply-Accumulate (MAC) operations per inference. Fortunately, reduced precision has demonstrated large benefits with low impact on accuracy, paving the way towards processing in mobile devices and IoT nodes. To this end, various precision-scalable MAC architectures optimized for neural networks have recently been proposed. Yet, it has been hard to comprehend their differences and make a fair judgment of their relative benefits as they have been implemented with different technologies and performance targets. To overcome this, this work exhaustively reviews the state-of-the-art precision-scalable MAC architectures and unifies them in a new taxonomy. Subsequently, these different topologies are thoroughly benchmarked in a 28nm commercial CMOS process, across a wide range of performance targets …