Neuromorphic Computing Applications in Robotics

Deep learning achieves remarkable success through training using massively labeled datasets. However, the high demands on the datasets impede the feasibility of deep learning in edge computing scenarios and suffer from the data scarcity issue. Rather than relying on labeled data, animals learn by interacting with their surroundings and memorizing the relationships between events and objects. This learning paradigm is referred to as associative learning. The successful implementation of associative learning imitates self-learning schemes analogous to animals which resolve the challenges of deep learning. Current state-of-the-art implementations of associative memory are limited to simulations with small-scale and offline paradigms. Thus, this work implements associative memory with an Unmanned Ground Vehicle (UGV) and neuromorphic hardware, specifically Intel’s Loihi, for an online learning scenario. This system emulates the classic associative learning in rats using the UGV in place of the rats. In specific, it successfully reproduces the fear conditioning with no pretraining procedure or labeled datasets. The UGV is rendered capable of autonomously learning the cause-and-effect relationship of the light stimulus and vibration stimulus and exhibiting a movement response to demonstrate the memorization. Hebbian learning dynamics are used to update the synaptic weights during the associative learning process. The Intel Loihi chip is integrated with this online learning system for processing visual signals with a specialized neural assembly. While processing, the Loihi’s average power usages for computing logic and memory are 30 mW and 29 mW, respectively

Calculations of Vacancy Diffusivity in WO3

The memristor is viewed as a promising material to store digital information and has analog applications that drew researchers’ attention. Researchers explored the possibilities of using memristors to simulate synapses in the human brain. WO3 is one of the materials that can make memristors. Based on the mechanism of memristors, we know the motion of defects in WO3 changes the Schottky barrier and the current; thus, it can make the switch between high resistance state, HRS, and low resistance state, LRS. This paper will explore vacancy diffusivity in WO3. In this research, we concentrate on the cubic and monoclinic structure of WO3. We use the first principle density functional theory or DFT, and hybrid DFT to calculate the formation energy of different charge states of oxygen vacancies in WO3 and plot the graph of Fermi level to find the charge state with the lowest formation energy conditions. We use the nudged elastic band method to get the energy barrier for the vacancies to migrate inside the structure

A physics-oriented memristor model with the coexistence of NDR effect and RS memory behavior for bio-inspired computing

Bio-inspired computing promises fundamentally different ways to advances in artificial intelligence with extreme energy efficiency. Memristive technologies due to the non-volatility, high density, low-power, and synaptic bionic properties can help in realizing bio-inspired architecture and its hardware implementation. This paper proposes a novel physics-oriented memristor model with coexistence of negative differential resistance (NDR) effect and resistive switching (RS) memory behavior for bio-inspired computing. Firstly, an Ag/TiOx/FTO memristor is fabricated using sol-gel and magnetron sputtering method, and its performance test demonstrates that the coexistence of NDR effect and RS memory behavior can be modulated by the moisture. Then, a physical-oriented memristor model is constructed, which provides the possibility to explore the dynamics of the coexistence of NDR effect and RS memory behavior in simulation. Furthermore, a memristor-based affective computing circuit emulating the process of human affective associative learning is designed. The experiment demonstrates that the coexistence of NDR effect and RS memory behavior can change the memory time without additional circuit and cost, which is expected to realize the automatic conversion from short-term memory to long-term memory in bio-inspired computing.National Natural Science Foundation of China under Grant 62001149 and Natural Science Foundation of Zhejiang Province under Grant LQ21F010009

Implementation of Associative Memory Learning in Mobile Robots Using Neuromorphic Computing

Fear conditioning is a behavioral paradigm of learning to predict aversive events. It is a form of associative learning that memorizes an undesirable stimulus (e.g., an electrical shock) and a neutral stimulus (e.g., a tone), resulting in a fear response (such as running away) to the originally neutral stimulus. The association of concurrent events is implemented by strengthening the synaptic connection between the neurons. In this paper, with an analogous methodology, we reproduce the classic fear conditioning experiment of rats using mobile robots and a neuromorphic system. In our design, the acceleration from a vibration platform substitutes the undesirable stimulus in rats. Meanwhile, the brightness of light (dark vs. light) is used for a neutral stimulus, which is analogous to the neutral sound in fear conditioning experiments in rats. The brightness of the light is processed with sparse coding in the Intel Loihi chip. The simulation and experimental results demonstrate that our neuromorphic robot successfully, for the first time, reproduces the fear conditioning experiment of rats with a mobile robot. The work exhibits a potential online learning paradigm with no labeled data required. The mobile robot directly memorizes the events by interacting with its surroundings, essentially different from data-driven methods