Temporal correlation detection using computational phase-change memory

Abstract

Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. As computation becomes increasingly data centric and the scalability limits in terms of performance and power are being reached, alternative computing paradigms with collocated computation and storage are actively being sought. A fascinating such approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. We present an experimental demonstration using one million phase change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Its result is imprinted in the conductance states of the memory devices. The results of using such a computational memory for processing real-world data sets show that this co-existence of computation and storage at the nanometer scale could enable ultra-dense, low-power, and massively-parallel computing systems.

Monatomic phase change memory

“…The retention time we have achieved already meets the requirements for some computing-in-memory tasks 30 and is orders of magnitude higher than that of dynamic random access memory (see supplementary information section 'Retention times in different memory applications'). For enhanced stability of the amorphous state, besides a further well-controlled reduction of the thickness, systematic investigations of alternative neighbouring materials with particular focus on their atomic-scale roughness 31 and rigidity will be instrumental in enhancing amorphous stability.…”

Fast Spiking of a Mott VO2–Carbon Nanotube Composite Device

“…4,[9][10][11] For instance, adjusting the analog node weights of a neural network by small increments in order to enable high precision will require precise and tunable low energy pulses, especially in networks that use memristors such as phase change memory or oxide ionic resistive switches. 12,13 Partly owing to the absence of compact circuits that can produce such tunable low-energy pulses, even the best memristor-based neural networks have had to implement elaborate transistor-based circuits at every node of very large networks, making the system's efficiency far from ideal. 14 Instead, compact spiking systems without transistors can be constructed by exploiting transient dynamics and/or electronic instabilities, for instance, the temporally abrupt resistance changes during a Mott insulator-…”

Artificial Perception Built on Memristive System: Visual, Auditory, and Tactile Sensations

“…At the device level, memristors stand out as frontrunners in terms of fast accessing speed (<85 ps in Figure a), ultralow power consumption (<100 fJ event −1 in Figure b), excellent scalability (linewidth <2 nm), high density (4.5 Tbit in −2 in Figure c), and high endurance (>10 12 in Figure d) . At the array level, the continuous analogue conductance tunability (Figure e) enables single‐step vector–matrix multiplication operation, which is the basis for designing a formidable calculation function for linear and differential equations …”