  Neuromorphic systems or dedicated hardware for neuromorphic computing is
getting popular with the advancement in research on different device materials
for synapses, especially in crossbar architecture and also algorithms specific
or compatible to neuromorphic hardware. Hence, an automated mapping of any deep
neural network onto the neuromorphic chip with crossbar array of synapses and
an efficient debugging framework is very essential. Here, mapping is defined as
the deployment of a section of deep neural network layer onto a neuromorphic
core and the generation of connection lists among population of neurons to
specify the connectivity between various neuromorphic cores on the neuromorphic
chip. Debugging is the verification of computations performed on the
neuromorphic chip during inferencing. Together the framework becomes Mapping
and Debugging (MaD) framework. MaD framework is quite general in usage as it is
a Python wrapper which can be integrated with almost every simulator tools for
neuromorphic chips. This paper illustrates the MaD framework in detail,
considering some optimizations while mapping onto a single neuromorphic core. A
classification task on MNIST and CIFAR-10 datasets are considered for test case
implementation of MaD framework.
