Inspired by the human brain, nonvolatile memories (NVMs)–based neuromorphic computing emerges as a promising paradigm to build power-efficient computing hardware for artificial intelligence. However, existing NVMs still suffer from physically imperfect device characteristics. In this work, a topotactic phase transition random-access memory (TPT-RAM) with a unique diffusive nonvolatile dual mode based on SrCoOx is demonstrated. The reversible phase transition of SrCoOx is well controlled by oxygen ion migrations along the highly ordered oxygen vacancy channels, enabling reproducible analog switching characteristics with reduced variability. Combining density functional theory and kinetic Monte Carlo simulations, the orientation-dependent switching mechanism of TPT-RAM is investigated synergistically. Furthermore, the dual-mode TPT-RAM is used to mimic the selective stabilization of developing synapses and implement neural network pruning, reducing ~84.2% of redundant synapses while improving the image classification accuracy to 99%. Our work points out a new direction to design bioplausible memristive synapses for neuromorphic computing.
In the long pursuit of smart robotics, it has been envisioned
to
empower robots with human-like senses, especially vision and touch.
While tremendous progress has been made in image sensors and computer
vision over the past decades, tactile sense abilities are lagging
behind due to the lack of large-scale flexible tactile sensor array
with high sensitivity, high spatial resolution, and fast response.
In this work, we have demonstrated a 64 × 64 flexible tactile
sensor array with a record-high spatial resolution of 0.9 mm (equivalently
28.2 pixels per inch) by integrating a high-performance piezoresistive
film (PRF) with a large-area active matrix of carbon nanotube thin-film
transistors. PRF with self-formed microstructures exhibited high pressure-sensitivity
of ∼385 kPa–1 for multi-walled carbon nanotubes
concentration of 6%, while the 14% one exhibited fast response time
of ∼3 ms, good linearity, broad detection range beyond 1400
kPa, and excellent cyclability over 3000 cycles. Using this fully
integrated tactile sensor array, the footprint maps of an artificial
honeybee were clearly identified. Furthermore, we hardware-implemented
a smart tactile system by integrating the PRF-based sensor array with
a memristor-based computing-in-memory chip to record and recognize
handwritten digits and Chinese calligraphy, achieving high classification
accuracies of 98.8% and 97.3% in hardware, respectively. The integration
of sensor networks with deep learning hardware may enable edge or
near-sensor computing with significantly reduced power consumption
and latency. Our work could empower the building of large-scale intelligent
sensor networks for next-generation smart robotics.
Hardware implementation in resource-efficient reservoir computing is of great interest for neuromorphic engineering. Recently, various devices have been explored to implement hardware-based reservoirs. However, most studies were mainly focused on the reservoir layer, whereas an end-to-end reservoir architecture has yet to be developed. Here, we propose a versatile method for implementing cyclic reservoirs using rotating elements integrated with signal-driven dynamic neurons, whose equivalence to standard cyclic reservoir algorithm is mathematically proven. Simulations show that the rotating neuron reservoir achieves record-low errors in a nonlinear system approximation benchmark. Furthermore, a hardware prototype was developed for near-sensor computing, chaotic time-series prediction and handwriting classification. By integrating a memristor array as a fully-connected output layer, the all-analog reservoir computing system achieves 94.0% accuracy, while simulation shows >1000× lower system-level power than prior works. Therefore, our work demonstrates an elegant rotation-based architecture that explores hardware physics as computational resources for high-performance reservoir computing.
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