xnor-net

**XNOR-Net** is an **optimized binary neural network architecture** — that approximates full-precision convolutions using XNOR (exclusive-NOR) operations and popcount, achieving ~58x computational speedup with a carefully designed scaling factor to reduce accuracy loss. **What Is XNOR-Net?** - **Innovation**: Introduces a real-valued scaling factor $alpha$ per filter. $Conv approx alpha cdot XNOR(sign(W), sign(X))$. - **Reason**: Pure binary ($pm 1$) loses magnitude information. The scaling factor $alpha$ (computed analytically from the filter) restores some of this information. - **Result**: Significantly better accuracy than naive BNNs, closer to full-precision. **Why It Matters** - **Practical BNNs**: Made binary networks accurate enough to be taken seriously for real deployment. - **Speed**: XNOR + popcount is natively supported on all modern CPUs (SSE, AVX instructions). - **Memory**: 32x compression of both weights AND activations. **XNOR-Net** is **logic-gate deep learning** — reducing the multiply-accumulate heart of neural networks to simple bitwise boolean operations.