Efficient Embedded Intelligence

Abstract

Vision Transformers perform strongly across computer vision tasks but often require too much compute and memory for embedded deployment. Binary quantization cuts these costs by constraining weights and activations to a single bit, at the expense of accuracy. We investigate whether the budget freed by binarization can be reinvested into additional model width to recover that lost accuracy. Using the BHViT-Tiny architecture on the Oxford-IIIT Pet dataset, we first isolate the accuracy gap caused by quantization alone by comparing a full-precision reference against its binarized counterpart at identical width, and then scale width within the freed budget to measure how much of this gap can be recovered by width. We find that binarization at the base width costs 7.1 points of Top-1 accuracy, and that tripling the width recovers 4.9 of these points while remaining at a theoretical 3.5× and 6.7× reduction in memory and compute relative to the full-precision reference. The wider binary model thus approaches full-precision accuracy at a fraction of its cost. Additionally, keeping the downsampling layers in full precision recovers a further 1.1 points at a cost still well within budget, narrowing the gap to 1.1 points and indicating that part of the residual loss stems from a precision bottleneck rather than from a global lack of capacity. Our results establish width scaling as an effective strategy for reducing the binarization accuracy gap, offering a promising path toward the resource-constrained deployment of Vision Transformers.