Brain-inspired computing systems: a systematic literature review

Brain-inspired computing is a growing and interdisciplinary area of research that investigates how the computational principles of the biological brain can be translated into hardware design to achieve improved energy efficiency. Brain-inspired computing encompasses various subfields, including neuromorphic and in-memory computing, that have been shown to outperform traditional digital hardware in executing specific tasks. With the rising demand for more powerful yet energy-efficient hardware for large-scale artificial neural networks, brain-inspired computing is emerging as a promising solution for enabling energy-efficient computing and expanding AI to the edge. However, the vast scope of the field has made it challenging to compare and assess the effectiveness of the solutions compared to state-of-the-art digital counterparts. This systematic literature review provides a comprehensive overview of the latest advances in brain-inspired computing hardware. To ensure accessibility for researchers from diverse backgrounds, we begin by introducing key concepts and pointing out respective in-depth topical reviews. We continue with categorizing the dominant hardware platforms. We highlight various studies and potential applications that could greatly benefit from brain-inspired computing systems and compare their reported computational accuracy. Finally, to have a fair comparison of the performance of different approaches, we employ a standardized normalization approach for energy efficiency reports in the literature. Graphical abstract Unconventional computing, including its four major, partly overlapping, brain-inspired computating frameworks: In-memory, neuromorphic, reservoir, and hyperdimensional computing