  Quantization-aware training (QAT) and Knowledge Distillation (KD) are
combined to achieve competitive performance in creating low-bit deep learning
models. However, existing works applying KD to QAT require tedious
hyper-parameter tuning to balance the weights of different loss terms, assume
the availability of labeled training data, and require complex, computationally
intensive training procedures for good performance. To address these
limitations, this paper proposes a novel Self-Supervised Quantization-Aware
Knowledge Distillation (SQAKD) framework. SQAKD first unifies the forward and
backward dynamics of various quantization functions, making it flexible for
incorporating various QAT works. Then it formulates QAT as a co-optimization
problem that simultaneously minimizes the KL-Loss between the full-precision
and low-bit models for KD and the discretization error for quantization,
without supervision from labels. A comprehensive evaluation shows that SQAKD
substantially outperforms the state-of-the-art QAT and KD works for a variety
of model architectures. Our code is at: https://github.com/kaiqi123/SQAKD.git.
