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The Perspective explores the future design of lifelong learning artificial intelligence (AI) accelerators that are intended for deployment in untethered environments, identifying key desirable capabilities for such edge AI accelerators and guidance on metrics to evaluate them.
This Perspective explores the development of metal halide perovskite transistors, examining the properties of halide perovskites and key perovskite transistors, and considering the challenges that exist in developing next-generation electronics and circuits using these devices.
This Perspective explores the potential of directly mapping computational problems in machine learning to materials and device properties, and proposes metrics to facilitate comparisons between different solutions to machine learning tasks.
This Perspective explores the use of flexible electronics in the development of brain–computer interfaces, considering their potential impact on neuroscience, neuroprosthetic control, bioelectronic medicine, and brain and machine intelligence integration.
