This paper describes a hardware implementation of threshold network ensembles (TNE) for classification applications. We first describe the algorithm and compare its performance with those of individual classifiers such as binary neural network and support vector machine (SVM). The effect of limited precision on the performance of threshold network ensembles is also investigated. The proposed multi-precision architecture is then mapped into a scalable systolic architecture implemented first on a single VLSI chip. The modularity and the easy programmability of the basic chip has made possible the extension of the architecture to a low cost multi-chip solution. We propose a 3D packaged circuit in which 12 basic chips have been integrated into a very compact volume of $(2 \times 2 \times 0.7) cm^3$. Successful operation of the 3D prototype is demonstrated through experimental test results of the chip.