MaD: Mapping and debugging framework for implementing deep neural network onto a neuromorphic chip with crossbar array of synapses
This addresses the need for efficient mapping and debugging tools for researchers and engineers implementing neural networks on neuromorphic hardware, though it is incremental as it builds on existing simulator tools.
The paper tackles the problem of automating the deployment and verification of deep neural networks on neuromorphic chips with crossbar arrays, resulting in a general Python-based framework (MaD) that integrates with simulators and demonstrates test cases on MNIST and CIFAR-10 datasets.
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.