  We propose a design methodology to facilitate fault tolerance of deep
learning models. First, we implement a many-core fault-tolerant neuromorphic
hardware design, where neuron and synapse circuitries in each neuromorphic core
are enclosed with astrocyte circuitries, the star-shaped glial cells of the
brain that facilitate self-repair by restoring the spike firing frequency of a
failed neuron using a closed-loop retrograde feedback signal. Next, we
introduce astrocytes in a deep learning model to achieve the required degree of
tolerance to hardware faults. Finally, we use a system software to partition
the astrocyte-enabled model into clusters and implement them on the proposed
fault-tolerant neuromorphic design. We evaluate this design methodology using
seven deep learning inference models and show that it is both area and power
efficient.
