Non–von Neumann multi-input spike signal processing enabled by an artificial synaptic multiplexer

Abstract

Multiplexing is essential for technologies that require processing of a large amount of information in real time. Here, we present an artificial synaptic multiplexing unit capable of realizing parallel multi-input control system. Ion gel was used as a dielectric layer of the artificial synaptic multiplexing unit because of its ionic property, allowing multigating for parallel input. A closed-loop control system that enables multi-input–based feedback for actuator bending control was realized by incorporating an ion gel–based artificial synaptic multiplexing unit, an actuator, and a bending angle sensor. The proposed multi-input control system could simultaneously process input and feedback signals, offering a breakthrough in industries in which the processing of vast amounts of streaming data is essential.

Electrochemical Analysis of Ion Effects on Electrolyte-Gated Synaptic Transistor Characteristics

“…Additionally, to process a wide variety of input signals, it is necessary to have a near-zero AR and a wide dynamic range. , The values of AR follow a trend analogous to that of the NL, with the K-AGTs having the lowest value (0.396) and the Na- and Li-AGTs at 32 pulses having the highest values (0.454 and 0.646, respectively; see Note S2, Supporting Information). According to Figure f, the dynamic range for the same pulse conditions is 11.31, which is the highest for Li-AGTs, and decreases to 8.13 and 6.22 in the Na- and Li-AGTs, respectively.…”

Artificial Neuron Devices

“…Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). (d–h) Reproduced with permission from ref . Copyright 2022, The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science.…”

“…Except for sensing of a single physical or chemical parameter, multimodal sensing of different parameters is even more important, which is essential for the decision-making in artificial intelligent systems. − This trend requires the increasing number of sensors in the systems, which further causes the complexity in the whole frame. Currently, conventional strategies to address this challenge use CMOS devices to make the system less complex, such as proportional integration differential-based control system and a parallel-to-serial converter. , However, such strategies are quite limited in terms of serial processing. , Alternatively, multiplex sensing has been regarded as a promising approach to overcome this limitation, which is able to processing in parallel. − Ho et al developed an artificial synaptic multiplexing closed-loop control system, which can process various data from different sensor inputs, as shown in Figure d . There are three components in this system: the sensor, actuator, and multiplexing unit, which are all fabricated in a fiber form.…”

“…569−571 Ho et al developed an artificial synaptic multiplexing closed-loop control system, which can process various data from different sensor inputs, as shown in Figure 30d. 546 There are three components in this system: the sensor, actuator, and multiplexing unit, which are all fabricated in a fiber form. The compactness of this on-fiber close-loop control system can enable various applications in soft robotics and prosthetic hand.…”

Multiplexed Complementary Signal Transmission for a Self‐Regulating Artificial Nervous System

“…The artificial nervous system essentially mimics the efficient signal processing of biological neurons, which thus has been widely studied as a method to overcome the various chronic limitations of conventional CMOS based systems. [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ] Especially, the neuromorphic system has a strong advantage in achieving portable, human‐friendly, and ubiquitous healthcare environments, owing to the beneficial traits in power consumption and device integration of synaptic processing. Beyond the simple mimicry of synaptic operations, recently, the primary focus of neuromorphic systems research has shifted to a more systematic simulation of the whole biological systems such as stimulus‐response mechanisms.…”