  Efficient inference of Deep Neural Networks (DNNs) on resource-constrained
edge devices is essential. Quantization and sparsity are key algorithmic
techniques that translate to repetition and sparsity within tensors at the
hardware-software interface. This paper introduces the concept of
repetition-sparsity trade-off that helps explain computational efficiency
during inference. We propose Signed Binarization, a unified co-design framework
that synergistically integrates hardware-software systems, quantization
functions, and representation learning techniques to address this trade-off.
Our results demonstrate that Signed Binarization is more accurate than
binarization with the same number of non-zero weights. Detailed analysis
indicates that signed binarization generates a smaller distribution of
effectual (non-zero) parameters nested within a larger distribution of total
parameters, both of the same type, for a DNN block. Finally, our approach
achieves a 26% speedup on real hardware, doubles energy efficiency, and reduces
density by 2.8x compared to binary methods for ResNet 18, presenting an
alternative solution for deploying efficient models in resource-limited
environments.
