Qloader ✦

Have a phone stuck in a boot loop with a corrupted bootloader? Traditional recovery won’t work. Fastboot won’t respond. But . It’s the last line of defense before a device becomes a paperweight. For this reason, it’s widely used with tools like MiFlash (for Xiaomi) and Odin (for some Qualcomm Samsung devices).

Here, $T(l, b_l)$ represents the estimated inference time for layer $l$ with bit-width $b_l$. QLoader constructs a lookup table of hardware benchmarks for the target device (e.g., ARM Cortex-M, NVIDIA Jetson) to estimate $T$. We solve this constrained optimization problem using a greedy heuristic:

Techniques such as pruning, weight sharing, and knowledge distillation have been widely explored. Pruning removes redundant weights, while distillation trains a smaller "student" network using a larger "teacher." While effective, these methods often require retraining or specific hardware sparse-matrix support. Quantization remains the most hardware-friendly approach as it exploits the integer arithmetic acceleration present in modern CPUs and NPUs.

The proliferation of Deep Neural Networks (DNNs) has revolutionized computer vision and natural language processing. However, the computational intensity and memory footprint of state-of-the-art models present significant barriers to deployment on resource-constrained edge devices. While model quantization is a prevalent technique for model compression, existing solutions often suffer from rigid bit-width constraints and significant accuracy degradation when aggressively quantized. This paper introduces , a novel, adaptive quantization loading framework designed to optimize the trade-off between inference latency and model accuracy. Unlike static quantization methods, QLoader implements a dynamic precision allocation strategy, coupled with a hardware-aware runtime loader. By profiling layer-wise sensitivity and hardware throughput, QLoader constructs a mixed-precision configuration that minimizes memory bandwidth usage while preserving the model's representational capacity. Our extensive experiments on the ImageNet dataset using ResNet-50, MobileNetV2, and BERT-Base demonstrate that QLoader achieves up to a 4.2x reduction in model size and a 2.8x improvement in inference speed compared to FP32 baselines, with an accuracy drop of less than 0.5%.

When porting a custom OS like LineageOS or GrapheneOS, developers often need to flash pre-release bootloaders or partition tables. If something goes wrong, QLoader is the safety net that lets them try again without destroying hardware.