We introduce a quantization-aware training algorithm that guarantees avoiding
numerical overflow when reducing the precision of accumulators during
inference. We leverage weight normalization as a means of constraining
parameters during training using accumulator bit width bounds that we derive.
We evaluate our algorithm across multiple quantized models that we train for
different tasks, showing that our approach can reduce the precision of
accumulators while maintaining model accuracy with respect to a floating-point
baseline. We then show that this reduction translates to increased design
efficiency for custom FPGA-based accelerators. Finally, we show that our
algorithm not only constrains weights to fit into an accumulator of
user-defined bit width, but also increases the sparsity and compressibility of
the resulting weights. Across all of our benchmark models trained with 8-bit
weights and activations, we observe that constraining the hidden layers of
quantized neural networks to fit into 16-bit accumulators yields an average
98.2% sparsity with an estimated compression rate of 46.5x all while
maintaining 99.2% of the floating-point performance.

这篇研究论文介绍了一种量化感知训练算法，该算法通过权重归一化约束参数，使用累加器位宽界限来避免数值溢出，在保持模型精度的同时，可以减少累加器的精度，并提高量化神经网络中的稀疏性和可压缩性，从而提高 FPGA 设计效率。