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Neuromorphic and Biomedical Engineering Laboratory

National Tsing Hua University, Taiwan


Our Recent News

賀 ! 本實驗室的論文被IEEE Access接受!

論文題目: "Gas Identification Algorithm Based on Dynamic Response Analysis of Metal Oxide Sensors under Temperature Modulation"

Posted by NBME on 06 03, 2024

賀 ! 本實驗室的論文被IEEE Access接受!

論文題目: "Implementation of a Double-Path Flipped Voltage Follower Cell for Voltage Conveyors"

Posted by NBME on 05 17, 2024

賀 ! 本實驗室的論文被IEEE Journal of Solid-State Circuits接受!

論文題目: "A 0.67-to-5.4 TSOPs/W Spiking Neural Network Accelerator with 128/256 Reconfigurable Neurons and Asynchronous Fully-Connected Synapses"

Posted by NBME on 05 07, 2024

賀 ! 本實驗室的論文被Respiratory Research接受!

論文題目: "Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study"

Posted by NBME on 05 06, 2024

賀 ! 鄭桂忠教授獲得「111年度清華-台達傑出人才講座」「2022 National Tsing Hua University-Delta Distinguished Talent」

Posted by NBME on 05 02, 2024

Pang-Chun Liu, Ting-I Chou, Shih-Wen Chiu, Kea-Tiong Tang, “A Low-Cost Air Quality Sensor Calibration Algorithm Using Self-Attention Network”, ISOEN 2024, accepted.

Posted by NBME on 03 22, 2024

Yen-Wen Chen, Rui-Hsuan Wang, Yu-Hsiang Cheng, Chih-Cheng Lu, "SUN: Dynamic Hybrid-Precision SRAM-Based CIM Accelerator with High Macro Utilization Using Structured Pruning Mixed-Precision Networks", IEEE TCAD, accepted

Posted by NBME on 02 19, 2024

Yu Hsuan Lin, Chao Yang Tang, Kea-Tiong Tang, “A 0.2-pJ/Sop Digital Spiking Neuromorphic Processor with Temporal Parallel Dataflow and Efficient Synapse Memory Compression”, 2024 IEEE 6th AICAS, accepted for Lecture presentation.

Posted by NBME on 02 19, 2024

Chia-Hua Hsu, Yu-Wei Lin, Yi-Hsin Liao, Liang-Kai Wang, Cheng-Jung Tsai, Kea-Tiong Tang, "A Low-Noise, Low-Power Neural Signal Amplifier for Deep Brain Stimulation System Chips Tolerating 3V Stimulation", 2024 IEEE ISCAS, accepted for Lecture presentation.

Posted by NBME on 01 16, 2024

賀 ! 本實驗室專題生楊士賢、余駿越、林允仲同學榮獲 112學年度「智慧感知聯網中心大專生專題競賽 影片人氣獎」

Posted by NBME on 12 15, 2023


Posted by NBME on 11 23, 2023

Tang, C. L., Chou, T. I., Yang, S. R., Lin, Y. J., Ye, Z. K., Chiu, S. W., Tang, K. T. (2023) “Development of a Nondestructive Moldy Coffee Beans Detection System Based on Electronic Nose”, IEEE Sensors Letters, 7(2), 1-4., accepted.

Posted by NBME on 11 23, 2023

賀 ! 本實驗室廖一心同學榮獲 112學年度「電機系碩、博士班入學成績優異獎學金」

Posted by NBME on 10 26, 2023

Ya-Han Fan, Ting-I Chou, Shih-Wen Chiu, Kea-Tiong Tang, “Gas Prediction Method Based on Dynamic Response Analysis of Metal Oxide Sensors under Temperature Modulation”, IEEE SENSORS 2023, accepted.

Posted by NBME on 10 22, 2023

Mu-Hsiang Kao, Shih-Wen Chiu, Meng-Rui Lee, Min Sun, Kea-Tiong Tang, “Deep Neural Network of E-Nose Sensor for Lung Cancer Classification”, 2023 IEEE Biosensors, 134438, accepted.

Posted by NBME on 10 22, 2023


Artificial Intelligence, Electronic Nose and Biomedical Implants

Artificial Intelligence

System Design

Neuromorphic AI Accelerator System Design

Algorithm Design

Neuromorphic AI Inference Chip Algorithm

Chip Design

Neuromorphic AI Accelerator Chip Design

Electronic Nose

Miniature Electronic Nose System

Integrated Micro Sensor Array

System-on-Chip and Package

Module, System and Algorithm

Application: Early Screening of Diseases

Chest physician

Gas analysis method

Diagnosis chip (VAP, COPD, LC)

Biomedical Implants

Circuit Design for Biomedical Devices

Data Transceiver, Wireless Power Transfer System, Stimulator

Analog to Digital Converter, Low Noise Amplifier, Digital Processor

Application: Deep Brain Stimulation

Implantable, Batteryless, Bidirectional Communications

Low Power Neural Recording and Stimulation

Small Device Size


Recent Publications

Development of a Nondestructive Moldy Coffee Beans Detection System Based on Electronic Nose

 Coffee drinks prepared from moldy coffee beans can adversely affect human health. No convenient screening method for detecting the smell of stale coffee beans exists. Accordingly, this study developed an electronic nose (E-nose) system for detecting the smell of coffee beans. This system comprises an environmental control system, a sensor array, and a data signal readout system. The system can distinguish various degrees of mold on coffee beans through the recognition of the smell of the coffee beans. In this study, we established a standard operating procedure to collect gas samples from coffee beans in a temperature- and humidity-controlled environment and recorded changes in the signals by using the sensor array after introducing the target gas. Features were first extracted from the collected data, then dimensionality reduction methods, such as principal component analysis and linear discriminant analysis, were applied to these features. Thus, their complexity was reduced, and the noise was eliminated. K-nearest neighbor and support vector machine were adopted as classification algorithms, and the classification accuracy of the proposed system reached 91.77%.

An Area-Efficient Accelerator for Non-Maximum Suppression

 Non-maximum suppression (NMS) is an essential part of the post-processing of object detectors. Most object detection models require NMS algorithms to filter overlapping candidate boxes belonging to the same object and reserve the box that best represents the object as the object representative box. However, the hardware for the standard NMS algorithm has some disadvantages, such as high complexity (especially when box numbers are significant), high latency, large area, and high power consumption. To solve these problems, we propose an efficient parallel hardware architecture, which uses a new sorting circuit with a ping-pong buffer and a new retention mechanism of the candidate box for the new NMS algorithm called distance over side-NMS (DoS-NMS). This architecture uses a PE group with a voting mechanism to simplify the algorithm for reducing the latency and area. Additionally, the PE group computes the DoS (non-intersection over union) of the candidate box and multiple object representatives in parallel, significantly reducing algorithm complexity and memory access cost. Experiments indicated that the algorithm runs on the chip with area of 0.75mm2 , power consumption of 68.41 mW, and normalized area efficiency that is 3.72 and 4.84 times higher than the two state-of-the-art methods, respectively.


Contact Us

 R812, Delta Building,Dept. of Electrical Engineering,
  National Tsing Hua University,No. 101, Sec. 2, Kuang-Fu
  Road, Hsinchu 30013, Taiwan