Modern CNNs' high computational demands hinder edge deployment, as traditional ``hard'' sparsity (skipping mathematical zeros) loses effectiveness in deep layers or with smooth activations like Tanh. We propose a ``soft sparsity'' paradigm using a hardware efficient Most Significant Bit (MSB) proxy to skip negligible non-zero multiplications. Integrated as a custom RISC-V instruction and evaluated on LeNet-5 (MNIST), this method reduces ReLU MACs by 88.42% and Tanh MACs by 74.87% with zero accuracy loss--outperforming zero-skipping by 5x. By clock-gating inactive multipliers, we estimate power savings of 35.2\% for ReLU and 29.96\% for Tanh. While memory access makes power reduction sub-linear to operation savings, this approach significantly optimizes resource-constrained inference.

翻译：现代CNN的高计算需求阻碍了边缘部署，因为传统的“硬”稀疏性（跳过数学零值）在深层网络或使用Tanh等平滑激活函数时效果不佳。我们提出一种“软稀疏”范式，利用硬件高效的最高有效位（MSB）代理来跳过可忽略的非零乘法。该方法作为定制RISC-V指令实现，在LeNet-5（MNIST）上的评估显示：在零精度损失前提下，ReLU的MAC运算减少88.42%，Tanh减少74.87%——性能超越零值跳过方法5倍。通过时钟门控闲置乘法器，我们估算ReLU可节能35.2%，Tanh节能29.96%。虽然内存访问导致功耗降低与运算节省呈次线性关系，但该方法显著优化了资源受限的推理场景。