• Transactions on Semiconductor Engineering
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• v.2 no.1
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• pp.1-8
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• 2024

When processing depthwise separable convolution, low utilization of processing elements (PEs) is one of the challenges of systolic array (SA). In this study, we propose a new SA architecture to maximize throughput in depthwise convolution. Moreover, the proposed SA performs subsequent pointwise convolution on the idle PEs during depthwise convolution computation to increase the utilization. After the computation, we utilize unused PEs to boost the remaining pointwise convolution. Consequently, the proposed 128x128 SA achieves a 4.05x and 1.75x speed improvement and reduces the energy consumption by 66.7 % and 25.4 %, respectively, compared to the basic SA and RiSA in MobileNetV3.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.43-53
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• 2024

Tomatoes are rich in nutrients like lycopene, β-carotene, and vitamin C. However, they often suffer from biological and environmental stressors, resulting in significant yield losses. Traditional manual plant health assessments are error-prone and inefficient for large-scale production. To address this need, we collected a comprehensive dataset covering the entire life span of tomato plants, annotated across 5 health states from 1 to 5. Our study introduces an Attention-Enhanced DS-ResNet architecture with Channel-wise attention and Grouped convolution, refined with new training techniques. Our model achieved an overall accuracy of 80.2% using 5-fold cross-validation, showcasing its robustness in precisely classifying the health states of tomato plants.