3 research outputs found
SCANN: Synthesis of Compact and Accurate Neural Networks
Deep neural networks (DNNs) have become the driving force behind recent
artificial intelligence (AI) research. An important problem with implementing a
neural network is the design of its architecture. Typically, such an
architecture is obtained manually by exploring its hyperparameter space and
kept fixed during training. This approach is time-consuming and inefficient.
Another issue is that modern neural networks often contain millions of
parameters, whereas many applications and devices require small inference
models. However, efforts to migrate DNNs to such devices typically entail a
significant loss of classification accuracy. To address these challenges, we
propose a two-step neural network synthesis methodology, called DR+SCANN, that
combines two complementary approaches to design compact and accurate DNNs. At
the core of our framework is the SCANN methodology that uses three basic
architecture-changing operations, namely connection growth, neuron growth, and
connection pruning, to synthesize feed-forward architectures with arbitrary
structure. SCANN encapsulates three synthesis methodologies that apply a
repeated grow-and-prune paradigm to three architectural starting points.
DR+SCANN combines the SCANN methodology with dataset dimensionality reduction
to alleviate the curse of dimensionality. We demonstrate the efficacy of SCANN
and DR+SCANN on various image and non-image datasets. We evaluate SCANN on
MNIST and ImageNet benchmarks. In addition, we also evaluate the efficacy of
using dimensionality reduction alongside SCANN (DR+SCANN) on nine small to
medium-size datasets. We also show that our synthesis methodology yields neural
networks that are much better at navigating the accuracy vs. energy efficiency
space. This would enable neural network-based inference even on
Internet-of-Things sensors.Comment: 13 pages, 8 figure