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We propose a design methodology to facilitate fault tolerance of deeplearning models. First, we implement a many-core fault-tolerant neuromorphichardware design, where neuron and synapse circuitries in each neuromorphic coreare enclosed with astrocyte circuitries, the star-shaped glial cells of thebrain that facilitate self-repair by restoring the spike firing frequency of afailed neuron using a closed-loop retrograde feedback signal. Next, weintroduce astrocytes in a deep learning model to achieve the required degree oftolerance to hardware faults. Finally, we use a system software to partitionthe astrocyte-enabled model into clusters and implement them on the proposedfault-tolerant neuromorphic design. We evaluate this design methodology usingseven deep learning inference models and show that it is both area and powerefficient.

A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networks