Hardware Realization of the Pattern Recognition with an Artificial Neuromorphic Device Exhibiting a Short-Term Memory

Abstract

Materials exhibiting memory or those capable of implementing certain learning schemes are the basic building blocks used in hardware realizations of the neuromorphic computing. One of the common goals within this paradigm assumes the integration of hardware and software solutions, leading to a substantial efficiency enhancement in complex classification tasks. At the same time, the use of unconventional approaches towards signal processing based on information carriers other than electrical carriers seems to be an interesting trend in the design of modern electronics. In this context, the implementation of light-sensitive elements appears particularly attractive. In this work, we combine the abovementioned ideas by using a simple optoelectronic device exhibiting a short-term memory for a rudimentary classification performed on a handwritten digits set extracted from the Modified National Institute of Standards and Technology Database (MNIST)(being one of the standards used for benchmarking of such systems). The input data was encoded into light pulses corresponding to black (ON-state) and white (OFF-state) pixels constituting a digit and used in this form to irradiate a polycrystalline cadmium sulfide electrode. An appropriate selection of time intervals between pulses allows utilization of a complex kinetics of charge trapping/detrapping events, yielding a short-term synaptic-like plasticity which in turn leads to the improvement of data separability. To the best of our knowledge, this contribution presents the simplest hardware realization of a classification system capable of performing neural network tasks without any sophisticated data processing.

Neuromorphic Liquids, Colloids, and Gels: A Review

“…These mechanism is valid locally for every junction between a CdS nanoparticle and a carbon nanotube, however the overall effect observed in the experiment results from the collective behavior of the hybrid material. Very similar effects have been observed for even simpler materials, like greenockite-hawleite mixtures [80] and used for more complex computational tasks, like classification of hand-written digits from MNIST database. Due to the specific nature of the device (spike-rate dependent plasticity) spacial patterns of hand-written digits were transformed into temporal patters of light pulse sequences -the arrangement of black and white pixels was translated into variable time intervals between subsequent pulses (Figure 10).…”

Optoelectronic Synaptic Devices for Neuromorphic Computing

“…Although the hardware implementation of the ANNs using optoelectronic synaptic devices requires further investigation, the pattern recognition can be simulated with algorithms nowadays. [ 135 ] The feasibility of pattern recognition is usually validated using the Modified National Institute of Standards and Technology (MNIST) handwritten digit images. The MNIST handwritten digit images with a resolution of 28 × 28 pixels are widely used to train ANNs.…”

Memristive Artificial Synapses for Neuromorphic Computing

“…Through image training, the neural network achieved 80.8% accuracy of recognition. However, the image recognition implemented by John et al was just realized with general software algorithms [ 144 ]. A perceptron classifier implemented with a realistic 2 × 10 titanium dioxide passive memristive crossbar circuit was experimentally demonstrated in 2013 [ 145 ].…”