Bio-inspired analog parallel array processor chip with programmable spatio-temporal dynamics

We introduce a new methodology and experimental implementations for real-time wave-based robot navigation in a complex, dynamically changing environment. The main idea behind the approach is to consider the robot arena as an excitable medium, in which moving objects-obstacles and the target-are represented by sites of autowave generation: the target generates attractive waves, while the obstacles repulsive ones. The moving robot detects traveling and colliding wave fronts and uses the information about dynamics of the autowaves to adapt its direction of collision-free motion toward the target. This approach allows us to achieve a highly adaptive robot behavior and thus an optimal path along which the robot reaches the target while avoiding obstacles. At the computational and experimental levels, we adopt principles of computation in reaction-diffusion (RD) nonlinear active media. Nonlinear media where autowaves are used for information processing purposes can therefore be considered as RD computing devices. In this paper, we design and experiment with three types of RD processors: experimental and computational Belousov-Zhabotinsky chemical processor, computational CNN processor, and experimental RD-CNN very large-scale integration chip-the complex analog and logic computing engine (CACE1k). We demonstrate how to experimentally implement robot navigation using space-time snapshots of active chemical medium and how to overcome low-speed limitation of this "wetware" implementation in CNN-based silicon processors.

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“…The prototype chip has been designed and fabricated in a 0.5 single-poly triple-metal CMOS technology [38]. Its dimensions are 9.27 8.45 sq.…”

Section: B Prototype Chip Datamentioning

confidence: 99%

Reaction-Diffusion Navigation Robot Control: From Chemical to VLSI Analogic Processors

Adamatzky

Arena

Basile

et al. 2004

IEEE Trans. Circuits Syst. I

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“…The sources are connected through capacitors to ground. We assume all transistors operate in weak inversion and are saturated, so the drain currents representing the layer are given by (9) where and are the gate and source voltages referenced to the bulk node. Differentiating with respect to time (10) where is the current entering the capacitor C. To implement the network, we equate the current flowing out of the capacitor with the sum on the right hand side of the element-wise product operator in (3).…”

Section: A Implementation Of State Variablesmentioning

confidence: 99%

“…A recent CMOS implementation of the circuit architecture in [6] contains six second-order cells [8]. One notable exception is the 32 32 cell two-layer cellular neural network (CNN) architecture described by Carmona et al [9]. The chip we report here differs from that chip in that it represents signals differentially and is implemented using transistors operating in weak, rather than strong, inversion.…”

Section: Introductionmentioning

confidence: 99%

Spatial Pattern Formation via Reaction–Diffusion Dynamics in 32<tex>$,times,$</tex>32<tex>$,times,$</tex>4 CNN Chip

Shi

Luo

2004

IEEE Trans. Circuits Syst. I

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Reaction-diffusion dynamics have been proposed to explain pattern-formation behavior in a variety of systems, e.g., chemical and biological. This paper describes a cellular neural network chip that exhibits spatially organized patterns of activity, which are formed by reaction diffusion. The chip contains four 32 32 cell arrays of transistors which are locally coupled and operate in weak inversion. Typical patterns consist of alternating regions of high-and low-drain currents with a preferred width, but no preferred orientation. Experimental, theoretical, and simulation results for this chip are in complete concordance, demonstrating that conventional very large-scale integration technology can be an ideal substrate for studying the spatio-temporal dynamics and applications of reaction-diffusion. The chip, which was fabricated in a 0.5-m process, settles to steady-state patterns within several hundred microseconds and dissipates 10.55 mW.

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“…Many artificial, physical, chemical, as well as biological systems have been represented by Cellular Neural Networks (CNN) models [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Its continuous-time analog operation allows real-time signal processing at high precision, while its local interaction between the constituent Processing Elements (PEs) obviate the need of global buses and long interconnects, leading to several digital and analog implementations of CNN architectures in the form of Very Large-Scale Integrated (VLSI) chips.…”

Section: Introductionmentioning

confidence: 99%

Memristor Computing Systems

2022

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Bio-inspired analog parallel array processor chip with programmable spatio-temporal dynamics

Cited by 6 publications

References 4 publications

Reaction-Diffusion Navigation Robot Control: From Chemical to VLSI Analogic Processors

Reaction-Diffusion Navigation Robot Control: From Chemical to VLSI Analogic Processors

Spatial Pattern Formation via Reaction–Diffusion Dynamics in 32<tex>$,times,$</tex>32<tex>$,times,$</tex>4 CNN Chip

Memristor Computing Systems

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Bio-inspired analog parallel array processor chip with programmable spatio-temporal dynamics

Cited by 6 publications

References 4 publications

Reaction-Diffusion Navigation Robot Control: From Chemical to VLSI Analogic Processors

Reaction-Diffusion Navigation Robot Control: From Chemical to VLSI Analogic Processors

Spatial Pattern Formation via Reaction–Diffusion Dynamics in 32&lt;tex&gt;$,times,$&lt;/tex&gt;32&lt;tex&gt;$,times,$&lt;/tex&gt;4 CNN Chip

Memristor Computing Systems

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Spatial Pattern Formation via Reaction–Diffusion Dynamics in 32<tex>$,times,$</tex>32<tex>$,times,$</tex>4 CNN Chip