Bioinspired Programming of Memory Devices for Implementing an Inference Engine

Cognitive tasks are essential for the modern applications of electronics, and rely on the capability to perform inference. The Von Neumann bottleneck is an important issue for such tasks, and emerging memory devices offer an opportunity to overcome this issue by fusing computing and memory, in nonvolatile instant on/off systems. A vision for accomplishing this is to use brain-inspired architectures, which excel at inference and do not differentiate between computing and memory. In this work, we use a neuroscience-inspired model of learning, spike-timing-dependent plasticity, to develop a bioinspired approach for programming memory devices, which naturally gives rise to an inference engine. The method is then adapted to different memory devices, including multivalued memories (cumulative memristive device, phase-change memory) and stochastic binary memories (conductive bridge memory, spin transfer torque magnetic tunnel junction). By means of system-level simulations, we investigate several applications, including image recognition and pattern detection within video and auditory data. We compare the results of the different devices. Stochastic binary devices require the use of redundancy, the extent of which depends tremendously on the considered task. A theoretical analysis allows us to understand how the various devices differ, and ties the inference engine to the machine learning algorithm of expectation-maximization. Monte Carlo simulations demonstrate an exceptional robustness of the inference engine with respect to device variations and other issues. A theoretical analysis explains the roots of this robustness. These results highlight a possible new bioinspired paradigm for programming emerging memory devices, allowing the natural learning of a complex inference engine. The physics of the memory devices plays an active role. The results open the way for a reinvention of the role of memory, when solving inference tasks.