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Ultralow-Power Machine Vision with Self-Powered Sensor Reservoir.

ABSTRACT: A neuromorphic visual system integrating optoelectronic synapses to perform the in-sensor computing is triggering a revolution due to the reduction of latency and energy consumption. Here it is demonstrated that the dwell time of photon-generated carriers in the space-charge region can be effectively extended by embedding a potential well on the shoulder of Schottky energy barrier. It permits the nonlinear interaction of photocurrents stimulated by spatiotemporal optical signals, which is necessary for in-sensor reservoir computing (RC). The machine vision with the sensor reservoir constituted by designed self-powered Au/P(VDF-TrFE)/Cs₂ AgBiBr₆ /ITO devices is competent for both static and dynamic vision tasks. It shows an accuracy of 99.97% for face classification and 100% for dynamic vehicle flow recognition. The in-sensor RC system takes advantage of near-zero energy consumption in the reservoir, resulting in decades-time lower training costs than a conventional neural network. This work paves the way for ultralow-power machine vision using photonic devices.

SUBMITTER: Lao J

PROVIDER: S-EPMC9130913 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Ultralow-Power Machine Vision with Self-Powered Sensor Reservoir.

Lao Jie J Yan Mengge M Tian Bobo B Jiang Chunli C Luo Chunhua C Xie Zhuozhuang Z Zhu Qiuxiang Q Bao Zhiqiang Z Zhong Ni N Tang Xiaodong X Sun Linfeng L Wu Guangjian G Wang Jianlu J Peng Hui H Chu Junhao J Duan Chungang C

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20220313 15

A neuromorphic visual system integrating optoelectronic synapses to perform the in-sensor computing is triggering a revolution due to the reduction of latency and energy consumption. Here it is demonstrated that the dwell time of photon-generated carriers in the space-charge region can be effectively extended by embedding a potential well on the shoulder of Schottky energy barrier. It permits the nonlinear interaction of photocurrents stimulated by spatiotemporal optical signals, which is necess ...[more]

PMID: 35285175

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Project description:This study explores the analysis and modeling of energy consumption in the context of database workloads, aiming to develop an eco-friendly database management system (DBMS). It leverages vibration energy harvesting systems with self-sustaining wireless vibration sensors (WVSs) in combination with the least square support vector machine algorithm to establish an energy consumption model (ECM) for relational database workloads. Through experiments, the performance of self-sustaining WVS in providing power is validated, and the accuracy of the proposed ECM during the execution of Structured Query Language (SQL) statements is evaluated. The findings demonstrate that this approach can reliably predict the energy consumption of database workloads, with a maximum prediction error rate of 10% during SQL statement execution. Furthermore, the ECM developed for relational databases closely approximates actual energy consumption for query operations, with errors ranging from 1 to 4%. In most cases, the predictions are conservative, falling below the actual values. This finding underscores the high predictive accuracy of the ECM in anticipating relational database workloads and their associated energy consumption. Additionally, this paper delves into prediction accuracy under different types of operations and reveals that ECM excels in single-block read operations, outperforming multi-block read operations. ECM exhibits substantial accuracy in predicting energy consumption for SQL statements in sequential and random read modes, especially in specialized database management system environments, where the error rate for the sequential read model is lower. In comparison to alternative models, the proposed ECM offers superior precision. Furthermore, a noticeable correlation between model error and the volume of data processed by SQL statements is observed. In summary, the relational database ECM introduced in this paper provides accurate predictions of workload and database energy consumption, offering a theoretical foundation and practical guidance for the development of eco-friendly DBMS.

Dataset Information

Ultralow-Power Machine Vision with Self-Powered Sensor Reservoir.

Publications

Ultralow-Power Machine Vision with Self-Powered Sensor Reservoir.

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