An Energy-efficient Classifier via Boosted Spin Channel Networks

With diminishing energy and delay benefits via CMOS scaling, there is much interest in exploring the use of alternative state variables such as electronic spin. Multiple research efforts are underway exploring both Boolean and non-Boolean design space using spin devices in order to make their energy and delay benefits competitive to CMOS. In this paper, we propose spin channel networks (SCNs) - spin-based circuits that exploit exponential decay of spin current to efficiently realize multi-bit dot product computation. We show that proposed SCNs can be employed with adaptive boosting (AdaBoost) learning algorithm to efficiently realize a binary classifier for breast cancer detection. The proposed SCN implementation achieves 112 x and 14x lower energy per decision compared to the conventional all spin logic (ASL) and 20 nm CMOS designs, respectively, for identical decision throughput.