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Neuromorphic Computing has become tremendously popular due to its ability to solve certain classes of learning tasks better than traditional von-Neumann computers. Data-intensive classification and pattern recognition problems have been of special interest to Neuromorphic Engineers, as these problems present complex use-cases for Deep Neural Networks (DNNs) which are motivated from the architecture of the human brain, and employ densely connected neurons and synapses organized in a hierarchical manner. However, as these systems become larger in order to handle an increasing amount of data and higher dimensionality of features, the designs often become connectivity constrained. To solve this, the computation is divided into multiple cores/islands, called processing engines (PEs). Today, the communication among these PEs are carried out through a power-hungry network-on-chip (NoC), and hence the optimal distribution of these islands along with energy-efficient compute and communication strategies become extremely important in reducing the overall energy of the neuromorphic computer, which is currently orders of magnitude higher than the biological human brain. In this paper, we extensively analyze the choice of the size of the islands based on mixed-signal neurons/synapses for 3-8 bit-resolution within allowable ranges for system-level classification error, determined by the analog non-idealities (noise and mismatch) in the neurons, and propose strategies involving local and global communication for reduction of the system-level energy consumption. AC-coupled mixed-signal neurons are shown to have 10X lower non-idealities than DC-coupled ones, while the choice of number of islands are shown to be a function of the network, constrained by the analog to digital conversion (or vice-versa) power at the interface of the islands. The maximum number of layers in an island is analyzed and a global bus-based sparse connectivity is proposed, which consumes orders of magnitude lower power than the competing powerline communication techniques.

Energy-Efficient Deep Neural Networks with Mixed-Signal Neurons and Dense-Local and Sparse-Global Connectivity