Spiking Neural Networks (SNNs) are emerging as a promising energy-efficient alternative to Artificial Neural Networks (ANNs) due to their event-driven computation paradigm. However, recent advances toward large-scale high-performance SNNs inevitably lead to substantial memory and computational overhead. While quantization offers a potential solution, many quantization approaches fail to deliver verifiable efficiency gains on resource-constrained hardware platforms. In this paper, we propose a lightweight and hardware-friendly SNN that applies quantization to both weights and membrane potentials, termed HardF-SNN. Specifically, we first build a baseline model that adopts shared-scale quantization and batch normalization (BN) folding to simulate integer-only inference during training, since this baseline model has not been thoroughly discussed in previous SNN work. Although the baseline enables integer-arithmetic-only inference, it suffers from performance degradation and may even lead to training failure. To address these issues, we thoroughly analyze the problems caused by quantization and BN folding, and propose solutions to enhance the baseline’s performance. Specifically, we introduce proportional shared-scale quantization to enhance the representation capability, and propose an integer-only BN method to stabilize training convergence through integer arithmetic and bit-shifting operations. Extensive experiments show that HardF-SNN achieves an optimal balance between performance and efficiency, exhibiting excellent compatibility with mainstream hardware accelerators. To demonstrate its effectiveness on resource-constrained platforms, HardF-SNN is deployed on a dedicated FPGA-based hardware accelerator. Evaluation results indicate that our implementation surpasses current state-of-the-art accelerators.