Learning Compact Boolean Networks

Deploying machine learning in resource-constrained and latency-sensitive domains is challenging. Boolean networks offer a promising alternative to floating-point neural networks by using Boolean gates to substantially reduce cost and latency. However, learning compact and accurate Boolean networks remains hard because of their discrete, combinatorial structure. We address this through three novel, complementary contributions: (i) a parameter-free strategy for learning effective connections, (ii) a compact convolutional Boolean architecture that exploits spatial locality while requiring fewer Boolean operations than existing convolutional kernels, and (iii) an adaptive discretization procedure that reduces the accuracy drop when converting a continuously relaxed network into a discrete Boolean network. Across standard vision benchmarks, our method improves the Pareto frontier over prior state-of-the-art methods, achieving higher accuracy with up to $47\times$ fewer Boolean operations. This advantage extends to other modalities. On an FPGA, our model on MNIST achieves 99.38% accuracy with a latency of 6.48 ns, surpassing the prior state of the art in both accuracy and runtime while producing a $7\times$ smaller Verilog file.