https://scholars.lib.ntu.edu.tw/handle/123456789/632309
Title: | CIM-Based Smart Pose Detection Sensors | Authors: | Chou J.-J Chang T.-W Liu X.-Y Wu T.-Y Chen Y.-K Hsu Y.-T Chen C.-W TSUNG-TE LIU CHI-SHENG SHIH |
Keywords: | analogy computing; non-ideality errors; smart sensors | Issue Date: | 2022 | Journal Volume: | 22 | Journal Issue: | 9 | Source: | Sensors | Abstract: | The majority of digital sensors rely on von Neumann architecture microprocessors to process sampled data. When the sampled data require complex computation for 24 × 7, the processing element will a consume significant amount of energy and computation resources. Several new sensing algorithms use deep neural network algorithms and consume even more computation resources. High resource consumption prevents such systems for 24 × 7 deployment although they can deliver impressive results. This work adopts a Computing-In-Memory (CIM) device, which integrates a storage and analog processing unit to eliminate data movement, to process sampled data. This work designs and evaluates the CIM-based sensing framework for human pose recognition. The framework consists of uncertainty-aware training, activation function design, and CIM error model collection. The evaluation results show that the framework can improve the detection accuracy of three poses classification on CIM devices using binary weights from 33.3% to 91.5% while that on ideal CIM is 92.1%. Although on digital systems the accuracy is 98.7% with binary weight and 99.5% with floating weight, the energy consumption of executing 1 convolution layer on a CIM device is only 30,000 to 50,000 times less than the digital sensing system. Such a design can significantly reduce power consumption and enables battery-powered always-on sensors. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129406696&doi=10.3390%2fs22093491&partnerID=40&md5=b69ee52cf511e6ed91b7a6f6bd91c25f https://scholars.lib.ntu.edu.tw/handle/123456789/632309 |
ISSN: | 14248220 | DOI: | 10.3390/s22093491 | SDG/Keyword: | Deep neural networks; Digital storage; Gesture recognition; Analogy computing; Complex computation; Computation resources; Detection sensors; Digital sensors; Neumann architecture; Non-ideality error; Nonideality; Pose detection; Sampled data; Energy utilization |
Appears in Collections: | 資訊工程學系 |
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