Energy-Efficient Stochastic Computing with Superparamagnetic Tunnel Junctions

Superparamagnetic tunnel junctions (SMTJs) have emerged as a competitive, realistic nanotechnology to support novel forms of stochastic computation in CMOS-compatible platforms. One of their applications is to generate random bitstreams suitable for use in stochastic computing implementations. We describe a method for digitally programmable bitstream generation based on precharge sense amplifiers. This generator is significantly more energy efficient than SMTJ-based bitstream generators that tune probabilities with spin currents and a factor of 2 more efficient than related CMOS-based implementations. The true randomness of this bitstream generator allows us to use them as the fundamental units of a novel neural network architecture. To take advantage of the potential savings, we codesign the algorithm with the circuit, rather than directly transcribing a classical neural network into hardware. The flexibility of the neural network mathematics allows us to adapt the network to the explicitly energy-efficient choices we make at the device level. The result is a convolutional neural network design operating at approximately $150$ nJ per inference with $97\mathrm{\%}$ performance on the MNIST data set—a factor of 1.4 to 7.7 improvement in energy efficiency over comparable proposals in the recent literature.