Training for multi-resolution inference using reusable quantization terms

Low-resolution uniform quantization (e.g., 4-bit bitwidth) for both Deep Neural Network (DNN) weights and data has emerged as an important technique for efficient inference. Departing from conventional quantization, we describe a novel training approach to support inference at multiple resolutions by reusing a single set of quantization terms (the same set of nonzero bits in values). The proposed approach streamlines the training and supports dynamic selection of resolution levels during inference. We evaluate the method on a diverse range of applications including multiple CNNs on ImageNet, an LSTM on Wikitext-2, and YOLO-v5 on COCO. We show that models resulting from our multi-resolution training can support up to 10 resolutions with only a moderate performance reduction (e.g., ≤ 1%) compared to training them individually. Lastly, using an FPGA, we compare our multi-resolution multiplier-accumulator (mMAC) against other conventional MAC designs and evaluate the inference performance. We show that the mMAC design broadens the choices in trading off cost, efficiency, and latency across a range of computational budgets.