Symmetry-aware reservoir computing.

We demonstrate that matching the symmetry properties of a reservoir computer (RC) to the data being processed dramatically increases its processing power. We apply our method to the parity task, a challenging benchmark problem that highlights inversion and permutation symmetries, and to a chaotic system inference task that presents an inversion symmetry rule. For the parity task, our symmetry-aware RC obtains zero error using an exponentially reduced neural network and training data, greatly speeding up the time to result and outperforming artificial neural networks. When both symmetries are respected, we find that the network size N necessary to obtain zero error for 50 different RC instances scales linearly with the parity-order n. Moreover, some symmetry-aware RC instances perform a zero error classification with only N=1 for n≤7. Furthermore, we show that a symmetry-aware RC only needs a training data set with size on the order of (n+n/2) to obtain such a performance, an exponential reduction in comparison to a regular RC which requires a training data set with size on the order of n2^{n} to contain all 2^{n} possible n-bit-long sequences. For the inference task, we show that a symmetry-aware RC presents a normalized root-mean-square error three orders-of-magnitude smaller than regular RCs. For both tasks, our RC approach respects the symmetries by adjusting only the input and the output layers, and not by problem-based modifications to the neural network. We anticipate that the generalizations of our procedure can be applied in information processing for problems with known symmetries.

Cryogenic Memory Architecture Integrating Spin Hall Effect based Magnetic Memory and Superconductive Cryotron Devices.

One of the most challenging obstacles to realizing exascale computing is minimizing the energy consumption of L2 cache, main memory, and interconnects to that memory. For promising cryogenic computing schemes utilizing Josephson junction superconducting logic, this obstacle is exacerbated by the cryogenic system requirements that expose the technology's lack of high-density, high-speed and power-efficient memory. Here we demonstrate an array of cryogenic memory cells consisting of a non-volatile three-terminal magnetic tunnel junction element driven by the spin Hall effect, combined with a superconducting heater-cryotron bit-select element. The write energy of these memory elements is roughly 8 pJ with a bit-select element, designed to achieve a minimum overhead power consumption of about 30%. Individual magnetic memory cells measured at 4 K show reliable switching with write error rates below 10-6, and a 4 × 4 array can be fully addressed with bit select error rates of 10-6. This demonstration is a first step towards a full cryogenic memory architecture targeting energy and performance specifications appropriate for applications in superconducting high performance and quantum computing control systems, which require significant memory resources operating at 4 K.

Time domain mapping of spin torque oscillator effective energy.

Stochastic dynamics of spin torque oscillators can be described in terms of magnetization drift and diffusion over a current-dependent effective energy surface given by the Fokker-Planck equation. Here we present a method that directly probes this effective energy surface via time-resolved measurements of the microwave voltage generated by a spin torque oscillator. We show that the effective energy approach provides a simple recipe for predicting spectral linewidths and line shapes near the generation threshold. Our time domain technique also accurately measures the fieldlike component of spin torque in a wide range of the voltage bias values.

Voltage-induced ferromagnetic resonance in magnetic tunnel junctions.

We demonstrate excitation of ferromagnetic resonance in CoFeB/MgO/CoFeB magnetic tunnel junctions (MTJs) by the combined action of voltage-controlled magnetic anisotropy (VCMA) and spin transfer torque (ST). Our measurements reveal that GHz-frequency VCMA torque and ST in low-resistance MTJs have similar magnitudes, and thus that both torques are equally important for understanding high-frequency voltage-driven magnetization dynamics in MTJs. As an example, we show that VCMA can increase the sensitivity of an MTJ-based microwave signal detector to the sensitivity level of semiconductor Schottky diodes.