  When arranged in a crossbar configuration, resistive memory devices can be
used to execute MVM, the most dominant operation of many ML algorithms, in
constant time complexity. Nonetheless, when performing computations in the
analog domain, novel challenges are introduced in terms of arithmetic precision
and stochasticity, due to non-ideal circuit and device behaviour. Moreover,
these non-idealities have a temporal dimension, resulting in a degrading
application accuracy over time. Facing these challenges, we propose a novel
framework, named LionHeart, to obtain hybrid analog-digital mappings to execute
DL inference workloads using heterogeneous accelerators. The
accuracy-constrained mappings derived by LionHeart showcase, across different
DNNs and datasets, high accuracy and potential for speedup. The results of the
full system simulations highlight run-time reductions and energy efficiency
gains that exceed 6X, with a user-defined accuracy threshold with respect to a
fully digital floating point implementation.
