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A passive and low-complexity Compressed Sensing architecture based on a charge-redistribution SAR ADC
An innovative analog-to-digital converter (ADC) architecture is proposed, with the aim of acquiring an input signal according to the Compressed Sensing (CS) paradigm and without the need for dedicated active analog blocks. Its core is the capacitive array employed in traditional successive-approxima...
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Published in: | Integration (Amsterdam) 2020-11, Vol.75, p.40-51 |
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creator | Paolino, Carmine Prono, Luciano Pareschi, Fabio Mangia, Mauro Rovatti, Riccardo Setti, Gianluca |
description | An innovative analog-to-digital converter (ADC) architecture is proposed, with the aim of acquiring an input signal according to the Compressed Sensing (CS) paradigm and without the need for dedicated active analog blocks. Its core is the capacitive array employed in traditional successive-approximation-register (SAR) ADCs. Introducing only a few additional switches, the array can compute the linear combination of consecutive signal samples, as required by the CS encoding.
To manage the presence of leakage currents, which may impair signal reconstruction, a compensation circuit is considered, allowing close-to-ideal performance of the system when properly designed. A neural network-based decoding strategy is also analyzed, with up to 20 dB of additional reconstruction quality with respect to standard algorithms. Synthetic electrocardiogram signals are used to validate optimizations both at the hardware level in the encoding block and at the software level in the decoder.
•A novel analog-to-digital converter compatible with Compressed Sensing is proposed.•Avoiding the use of active analog blocks, the potential saving in energy is remarkable.•Degradation induced by leakage currents is countered by a compensation circuit.•A deep neural network-based decoder can be used to achieve performance close to ideal. |
doi_str_mv | 10.1016/j.vlsi.2020.05.007 |
format | article |
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To manage the presence of leakage currents, which may impair signal reconstruction, a compensation circuit is considered, allowing close-to-ideal performance of the system when properly designed. A neural network-based decoding strategy is also analyzed, with up to 20 dB of additional reconstruction quality with respect to standard algorithms. Synthetic electrocardiogram signals are used to validate optimizations both at the hardware level in the encoding block and at the software level in the decoder.
•A novel analog-to-digital converter compatible with Compressed Sensing is proposed.•Avoiding the use of active analog blocks, the potential saving in energy is remarkable.•Degradation induced by leakage currents is countered by a compensation circuit.•A deep neural network-based decoder can be used to achieve performance close to ideal.</description><identifier>ISSN: 0167-9260</identifier><identifier>EISSN: 1872-7522</identifier><identifier>DOI: 10.1016/j.vlsi.2020.05.007</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Algorithms ; Analog ; Analog to digital conversion ; Analog to digital converters ; Architecture ; Arrays ; Circuit design ; Circuits ; Compressed sensing ; Decoding ; Deep neural networks ; Electrocardiography ; Leakage ; Leakage compensation ; Leakage current ; Neural networks ; Sensors ; Signal processing ; Signal reconstruction ; Sub-Nyquist sampling ; Successive approximation register ; Switches</subject><ispartof>Integration (Amsterdam), 2020-11, Vol.75, p.40-51</ispartof><rights>2020</rights><rights>Copyright Elsevier BV Nov 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c279t-5187395af265f429597a0df8fa946c812870d043f45be3c2c6ee5d026874fc773</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Paolino, Carmine</creatorcontrib><creatorcontrib>Prono, Luciano</creatorcontrib><creatorcontrib>Pareschi, Fabio</creatorcontrib><creatorcontrib>Mangia, Mauro</creatorcontrib><creatorcontrib>Rovatti, Riccardo</creatorcontrib><creatorcontrib>Setti, Gianluca</creatorcontrib><title>A passive and low-complexity Compressed Sensing architecture based on a charge-redistribution SAR ADC</title><title>Integration (Amsterdam)</title><description>An innovative analog-to-digital converter (ADC) architecture is proposed, with the aim of acquiring an input signal according to the Compressed Sensing (CS) paradigm and without the need for dedicated active analog blocks. Its core is the capacitive array employed in traditional successive-approximation-register (SAR) ADCs. Introducing only a few additional switches, the array can compute the linear combination of consecutive signal samples, as required by the CS encoding.
To manage the presence of leakage currents, which may impair signal reconstruction, a compensation circuit is considered, allowing close-to-ideal performance of the system when properly designed. A neural network-based decoding strategy is also analyzed, with up to 20 dB of additional reconstruction quality with respect to standard algorithms. Synthetic electrocardiogram signals are used to validate optimizations both at the hardware level in the encoding block and at the software level in the decoder.
•A novel analog-to-digital converter compatible with Compressed Sensing is proposed.•Avoiding the use of active analog blocks, the potential saving in energy is remarkable.•Degradation induced by leakage currents is countered by a compensation circuit.•A deep neural network-based decoder can be used to achieve performance close to ideal.</description><subject>Algorithms</subject><subject>Analog</subject><subject>Analog to digital conversion</subject><subject>Analog to digital converters</subject><subject>Architecture</subject><subject>Arrays</subject><subject>Circuit design</subject><subject>Circuits</subject><subject>Compressed sensing</subject><subject>Decoding</subject><subject>Deep neural networks</subject><subject>Electrocardiography</subject><subject>Leakage</subject><subject>Leakage compensation</subject><subject>Leakage current</subject><subject>Neural networks</subject><subject>Sensors</subject><subject>Signal processing</subject><subject>Signal reconstruction</subject><subject>Sub-Nyquist sampling</subject><subject>Successive approximation register</subject><subject>Switches</subject><issn>0167-9260</issn><issn>1872-7522</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kMtOwzAQRS0EEqXwA6wssU4YO3GcSGyq8JSQkCisLdcZt67aJNhJoX-Pq7JmNaOZe-dxCLlmkDJgxe063W2CSzlwSEGkAPKETFgpeSIF56dkEkUyqXgB5-QihDUAsFyKCcEZ7XUIbodUtw3ddN-J6bb9Bn_csKd1TD2GgA2dYxtcu6Tam5Ub0AyjR7rQh1bXUk3NSvslJh4bFwbvFuPgYn0-e6ez-_qSnFm9CXj1F6fk8_Hho35OXt-eXurZa2K4rIZExIuzSmjLC2FzXolKamhsaXWVF6ZkvJTQQJ7ZXCwwM9wUiKIBXpQyt0bKbEpujnN7332NGAa17kbfxpWK54IxGf_OooofVcZ3IXi0qvduq_1eMVAHnGqtDjjVAacCoSLOaLo7mjDev3PoVTAOWxP_9ZGGajr3n_0Xk7F94Q</recordid><startdate>202011</startdate><enddate>202011</enddate><creator>Paolino, Carmine</creator><creator>Prono, Luciano</creator><creator>Pareschi, Fabio</creator><creator>Mangia, Mauro</creator><creator>Rovatti, Riccardo</creator><creator>Setti, Gianluca</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope></search><sort><creationdate>202011</creationdate><title>A passive and low-complexity Compressed Sensing architecture based on a charge-redistribution SAR ADC</title><author>Paolino, Carmine ; Prono, Luciano ; Pareschi, Fabio ; Mangia, Mauro ; Rovatti, Riccardo ; Setti, Gianluca</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c279t-5187395af265f429597a0df8fa946c812870d043f45be3c2c6ee5d026874fc773</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Analog</topic><topic>Analog to digital conversion</topic><topic>Analog to digital converters</topic><topic>Architecture</topic><topic>Arrays</topic><topic>Circuit design</topic><topic>Circuits</topic><topic>Compressed sensing</topic><topic>Decoding</topic><topic>Deep neural networks</topic><topic>Electrocardiography</topic><topic>Leakage</topic><topic>Leakage compensation</topic><topic>Leakage current</topic><topic>Neural networks</topic><topic>Sensors</topic><topic>Signal processing</topic><topic>Signal reconstruction</topic><topic>Sub-Nyquist sampling</topic><topic>Successive approximation register</topic><topic>Switches</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Paolino, Carmine</creatorcontrib><creatorcontrib>Prono, Luciano</creatorcontrib><creatorcontrib>Pareschi, Fabio</creatorcontrib><creatorcontrib>Mangia, Mauro</creatorcontrib><creatorcontrib>Rovatti, Riccardo</creatorcontrib><creatorcontrib>Setti, Gianluca</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Integration (Amsterdam)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Paolino, Carmine</au><au>Prono, Luciano</au><au>Pareschi, Fabio</au><au>Mangia, Mauro</au><au>Rovatti, Riccardo</au><au>Setti, Gianluca</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A passive and low-complexity Compressed Sensing architecture based on a charge-redistribution SAR ADC</atitle><jtitle>Integration (Amsterdam)</jtitle><date>2020-11</date><risdate>2020</risdate><volume>75</volume><spage>40</spage><epage>51</epage><pages>40-51</pages><issn>0167-9260</issn><eissn>1872-7522</eissn><abstract>An innovative analog-to-digital converter (ADC) architecture is proposed, with the aim of acquiring an input signal according to the Compressed Sensing (CS) paradigm and without the need for dedicated active analog blocks. Its core is the capacitive array employed in traditional successive-approximation-register (SAR) ADCs. Introducing only a few additional switches, the array can compute the linear combination of consecutive signal samples, as required by the CS encoding.
To manage the presence of leakage currents, which may impair signal reconstruction, a compensation circuit is considered, allowing close-to-ideal performance of the system when properly designed. A neural network-based decoding strategy is also analyzed, with up to 20 dB of additional reconstruction quality with respect to standard algorithms. Synthetic electrocardiogram signals are used to validate optimizations both at the hardware level in the encoding block and at the software level in the decoder.
•A novel analog-to-digital converter compatible with Compressed Sensing is proposed.•Avoiding the use of active analog blocks, the potential saving in energy is remarkable.•Degradation induced by leakage currents is countered by a compensation circuit.•A deep neural network-based decoder can be used to achieve performance close to ideal.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.vlsi.2020.05.007</doi><tpages>12</tpages></addata></record> |
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source | ScienceDirect Freedom Collection 2022-2024 |
subjects | Algorithms Analog Analog to digital conversion Analog to digital converters Architecture Arrays Circuit design Circuits Compressed sensing Decoding Deep neural networks Electrocardiography Leakage Leakage compensation Leakage current Neural networks Sensors Signal processing Signal reconstruction Sub-Nyquist sampling Successive approximation register Switches |
title | A passive and low-complexity Compressed Sensing architecture based on a charge-redistribution SAR ADC |
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