  Spiking Neural Networks (SNNs) have the potential for achieving low energy
consumption due to their biologically sparse computation. Several studies have
shown that the off-chip memory (DRAM) accesses are the most energy-consuming
operations in SNN processing. However, state-of-the-art in SNN systems do not
optimize the DRAM energy-per-access, thereby hindering achieving high
energy-efficiency. To substantially minimize the DRAM energy-per-access, a key
knob is to reduce the DRAM supply voltage but this may lead to DRAM errors
(i.e., the so-called approximate DRAM). Towards this, we propose SparkXD, a
novel framework that provides a comprehensive conjoint solution for resilient
and energy-efficient SNN inference using low-power DRAMs subjected to
voltage-induced errors. The key mechanisms of SparkXD are: (1) improving the
SNN error tolerance through fault-aware training that considers bit errors from
approximate DRAM, (2) analyzing the error tolerance of the improved SNN model
to find the maximum tolerable bit error rate (BER) that meets the targeted
accuracy constraint, and (3) energy-efficient DRAM data mapping for the
resilient SNN model that maps the weights in the appropriate DRAM location to
minimize the DRAM access energy. Through these mechanisms, SparkXD mitigates
the negative impact of DRAM (approximation) errors, and provides the required
accuracy. The experimental results show that, for a target accuracy within 1%
of the baseline design (i.e., SNN without DRAM errors), SparkXD reduces the
DRAM energy by ca. 40% on average across different network sizes.
