Pub Date : 2020-06-01DOI: 10.1109/newcas49341.2020.9159824
Kebria Naderi, Erwin H. T. Shad, M. Molinas, A. Heidari
In this paper, a low-noise low-power bio-potential amplifier for electromyogram (EMG) signals have designed and simulated in a commercially available 0.18 μm CMOS technology. In the first stage, a tunable band low-noise amplifier (TBLNA) is designed. In the second stage, a programmable gain amplifier (PGA) is utilized. Inside the TBLNA, by utilizing current splitting technique and current scaling technique in a current mirror operational transconductance amplifier (CM-OTA), a very good trade-off between power and noise is achieved. In addition, the second stage is designed to meet the maximum output swing. After post-layout simulation, the proposed amplifier has a variable gain between 40.2 dB to 57 dB while its high-pass and low-pass cutoff frequencies are 5-16 Hz and 1-1.75 kHz, respectively. Total input referred noise in the total bandwidth is 2.28 μVrms and the total current consumption is 1.35 μA at a 1.4 V supply voltage. Therefore, the noise efficiency factor (NEF) is 2.43. By utilizing a rail-to-rail structure and a noise efficient design, the power efficiency factor (PEF) of the proposed structure is 8.26 which is relatively lower than state-of-the-art EMG amplifiers while its output swing is 1.2 V. The minimum value of the common mode rejection ratio (CMRR) and power supply rejection ratio (PSRR) are 70 dB and 73 dB, respectively. Finally, the total area consumption without pads is 0.07 mm2.
{"title":"A Fully Tunable Low-power Low-noise and High Swing EMG Amplifier with 8.26 PEF","authors":"Kebria Naderi, Erwin H. T. Shad, M. Molinas, A. Heidari","doi":"10.1109/newcas49341.2020.9159824","DOIUrl":"https://doi.org/10.1109/newcas49341.2020.9159824","url":null,"abstract":"In this paper, a low-noise low-power bio-potential amplifier for electromyogram (EMG) signals have designed and simulated in a commercially available 0.18 μm CMOS technology. In the first stage, a tunable band low-noise amplifier (TBLNA) is designed. In the second stage, a programmable gain amplifier (PGA) is utilized. Inside the TBLNA, by utilizing current splitting technique and current scaling technique in a current mirror operational transconductance amplifier (CM-OTA), a very good trade-off between power and noise is achieved. In addition, the second stage is designed to meet the maximum output swing. After post-layout simulation, the proposed amplifier has a variable gain between 40.2 dB to 57 dB while its high-pass and low-pass cutoff frequencies are 5-16 Hz and 1-1.75 kHz, respectively. Total input referred noise in the total bandwidth is 2.28 μVrms and the total current consumption is 1.35 μA at a 1.4 V supply voltage. Therefore, the noise efficiency factor (NEF) is 2.43. By utilizing a rail-to-rail structure and a noise efficient design, the power efficiency factor (PEF) of the proposed structure is 8.26 which is relatively lower than state-of-the-art EMG amplifiers while its output swing is 1.2 V. The minimum value of the common mode rejection ratio (CMRR) and power supply rejection ratio (PSRR) are 70 dB and 73 dB, respectively. Finally, the total area consumption without pads is 0.07 mm2.","PeriodicalId":135163,"journal":{"name":"2020 18th IEEE International New Circuits and Systems Conference (NEWCAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123345790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-15DOI: 10.1109/newcas49341.2020.9159818
Mehdi Ahmadi, S. Vakili, J. M. P. Langlois
Convolutional Neural Networks (CNNs) have shown outstanding accuracy for many vision tasks during recent years. When deploying CNNs on portable devices and embedded systems, however, the large number of parameters and computations result in long processing time and low battery life. An important factor in designing CNN hardware accelerators is to efficiently map the convolution computation onto hardware resources. In addition, to save battery life and reduce energy consumption, it is essential to reduce the number of DRAM accesses since DRAM consumes orders of magnitude more energy compared to other operations in hardware. In this paper, we propose an energy-efficient architecture which maximally utilizes its computational units for convolution operations while requiring a low number of DRAM accesses. The implementation results show that the proposed architecture performs one image recognition task using the VGGNet model with a latency of 393 ms and only 251.5 MB of DRAM accesses.
{"title":"An Energy-Efficient Accelerator Architecture with Serial Accumulation Dataflow for Deep CNNs","authors":"Mehdi Ahmadi, S. Vakili, J. M. P. Langlois","doi":"10.1109/newcas49341.2020.9159818","DOIUrl":"https://doi.org/10.1109/newcas49341.2020.9159818","url":null,"abstract":"Convolutional Neural Networks (CNNs) have shown outstanding accuracy for many vision tasks during recent years. When deploying CNNs on portable devices and embedded systems, however, the large number of parameters and computations result in long processing time and low battery life. An important factor in designing CNN hardware accelerators is to efficiently map the convolution computation onto hardware resources. In addition, to save battery life and reduce energy consumption, it is essential to reduce the number of DRAM accesses since DRAM consumes orders of magnitude more energy compared to other operations in hardware. In this paper, we propose an energy-efficient architecture which maximally utilizes its computational units for convolution operations while requiring a low number of DRAM accesses. The implementation results show that the proposed architecture performs one image recognition task using the VGGNet model with a latency of 393 ms and only 251.5 MB of DRAM accesses.","PeriodicalId":135163,"journal":{"name":"2020 18th IEEE International New Circuits and Systems Conference (NEWCAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117182181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Conference Support and Sponsors","authors":"","doi":"10.1109/icbake.2011.65","DOIUrl":"https://doi.org/10.1109/icbake.2011.65","url":null,"abstract":"","PeriodicalId":135163,"journal":{"name":"2020 18th IEEE International New Circuits and Systems Conference (NEWCAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125323398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-07DOI: 10.1109/newcas49341.2020.9159784
Yanjun Cao, D. St-Onge, A. Zell, G. Beltrame
Localization and tracking are two very active areas of research for robotics, automation, and the Internet-of-Things. However, accurate tracking of a large number of devices in large areas with many rooms is very challenging: generally, one needs substantial infrastructure (infrared tracking systems, cameras, wireless antennas, etc.) for each room. This paper aims at covering a large number of devices distributed in many of small rooms, with minimal localization infrastructure. We use Ultra-wideband (UWB) technology in a device-2-device collaborative setting to develop a localization solution requiring a minimal number of fixed anchors. We present a strategy that autonomously shares the UWB network among devices and allows fast and accurate localization and tracking. We show results from an experimental campaign tracking visitors in the Chambord castle in France.
{"title":"Collaborative Localization and Tracking with Minimal Infrastructure","authors":"Yanjun Cao, D. St-Onge, A. Zell, G. Beltrame","doi":"10.1109/newcas49341.2020.9159784","DOIUrl":"https://doi.org/10.1109/newcas49341.2020.9159784","url":null,"abstract":"Localization and tracking are two very active areas of research for robotics, automation, and the Internet-of-Things. However, accurate tracking of a large number of devices in large areas with many rooms is very challenging: generally, one needs substantial infrastructure (infrared tracking systems, cameras, wireless antennas, etc.) for each room. This paper aims at covering a large number of devices distributed in many of small rooms, with minimal localization infrastructure. We use Ultra-wideband (UWB) technology in a device-2-device collaborative setting to develop a localization solution requiring a minimal number of fixed anchors. We present a strategy that autonomously shares the UWB network among devices and allows fast and accurate localization and tracking. We show results from an experimental campaign tracking visitors in the Chambord castle in France.","PeriodicalId":135163,"journal":{"name":"2020 18th IEEE International New Circuits and Systems Conference (NEWCAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126081861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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