  Neural networks (NNs) with intensive multiplications (e.g., convolutions and
transformers) are capable yet power hungry, impeding their more extensive
deployment into resource-constrained devices. As such, multiplication-free
networks, which follow a common practice in energy-efficient hardware
implementation to parameterize NNs with more efficient operators (e.g., bitwise
shifts and additions), have gained growing attention. However,
multiplication-free networks usually under-perform their vanilla counterparts
in terms of the achieved accuracy. To this end, this work advocates hybrid NNs
that consist of both powerful yet costly multiplications and efficient yet less
powerful operators for marrying the best of both worlds, and proposes
ShiftAddNAS, which can automatically search for more accurate and more
efficient NNs. Our ShiftAddNAS highlights two enablers. Specifically, it
integrates (1) the first hybrid search space that incorporates both
multiplication-based and multiplication-free operators for facilitating the
development of both accurate and efficient hybrid NNs; and (2) a novel weight
sharing strategy that enables effective weight sharing among different
operators that follow heterogeneous distributions (e.g., Gaussian for
convolutions vs. Laplacian for add operators) and simultaneously leads to a
largely reduced supernet size and much better searched networks. Extensive
experiments and ablation studies on various models, datasets, and tasks
consistently validate the efficacy of ShiftAddNAS, e.g., achieving up to a
+7.7% higher accuracy or a +4.9 better BLEU score compared to state-of-the-art
NN, while leading to up to 93% or 69% energy and latency savings, respectively.
Codes and pretrained models are available at
https://github.com/RICE-EIC/ShiftAddNAS.
