  Machine-learning algorithms have shown outstanding image recognition or
classification performance for computer vision applications. However, the
compute and energy requirement for implementing such classifier models for
large-scale problems is quite high. In this paper, we propose Feature Driven
Selective Classification (FALCON) inspired by the biological visual attention
mechanism in the brain to optimize the energy-efficiency of machine-learning
classifiers. We use the consensus in the characteristic features
(color/texture) across images in a dataset to decompose the original
classification problem and construct a tree of classifiers (nodes) with a
generic-to-specific transition in the classification hierarchy. The initial
nodes of the tree separate the instances based on feature information and
selectively enable the latter nodes to perform object specific classification.
The proposed methodology allows selective activation of only those branches and
nodes of the classification tree that are relevant to the input while keeping
the remaining nodes idle. Additionally, we propose a programmable and scalable
Neuromorphic Engine (NeuE) that utilizes arrays of specialized neural
computational elements to execute the FALCON based classifier models for
diverse datasets. The structure of FALCON facilitates the reuse of nodes while
scaling up from small classification problems to larger ones thus allowing us
to construct classifier implementations that are significantly more efficient.
We evaluate our approach for a 12-object classification task on the Caltech101
dataset and 10-object task on CIFAR-10 dataset by constructing FALCON models on
the NeuE platform in 45nm technology. Our results demonstrate significant
improvement in energy-efficiency and training time for minimal loss in output
quality.
