Design of Resource-Efficient AI Models through Parameter Reduction and Accuracy-Aware Compression
With the growing deployment of state-of-the-art deep neural networks in safety-critical, embedded and edge-computing applications, there is a strong incentive to design models that achieve high accuracy while maintaining very limited computational and memory budgets. We present a systematic study in resource-efficient AI model design addressing two orthogonal strategies of structured parameter reduction and accuracy-aware compression. Based on experiments on 6 benchmark datasets Image Net, CIFAR-10, GLUE (SST-2 and MNLI), MS COCO and Squad 1.1 we evaluate and compare pruning, quantization-aware training (QAT), knowledge distillation (KD), low-rank factorization and neural architecture search (NAS) in a systematic manner. The proposed hybrid pipeline includes structured pruning, INT8 quantization and task-specific knowledge distillation, which is benchmarked against standalone methods. Empirical results show that the proposed hybrid gives 4.5–5.2× inference speedup, 6–8× parameter reduction but just −0.2 to −0.3 percentage points accuracy drop compared to full-precision baselines on vision and language tasks. Five contextual analytical tables, capturing performance across the parameters latency alone, energy consumption alone and cross-task accuracy reinforce that Pareto-optimal results are consistently achieved for this hybrid method. Comparison with fundamental earlier research including Han et al. [5], Hinton et al. [12], Jacob et al. [9], Hu et al. [16], and Sanh et al. Now, looking at [20], it reinforces the idea of upper bound projection based approach for accuracy-oriented, multi-level compression. These results have immediate application to large-scale AI deployment on resource-limited hardware platforms, allowing AI democratization with fidelity.