An on-chip learning, low-power probabilistic spiking neural network with long-term memory
This paper describes an analog probabilistic spiking neural network (PSNN) circuit for portable and implanted applications which especially require low power, small area and on-chip learning to ensure good mobility, body safety and continually accurate classification. The circuit is implemented usin...
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| creator | Hung-Yi Hsieh Kea-Tiong Tang |
| description | This paper describes an analog probabilistic spiking neural network (PSNN) circuit for portable and implanted applications which especially require low power, small area and on-chip learning to ensure good mobility, body safety and continually accurate classification. The circuit is implemented using TSMC 0.18μm CMOS technology. Simulation results show that the circuit can learn linearly non-separable exclusive-or (xor) problem under 1V supply with only 3.8μW of power consumption. Long-term, multi-stage synaptic memory contains more information for a longer time in a single synapse. Comparison of the proposed PSNN with recent hardware neural networks is also provided. |
| doi_str_mv | 10.1109/BioCAS.2013.6679626 |
| format | Conference Proceeding |
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The circuit is implemented using TSMC 0.18μm CMOS technology. Simulation results show that the circuit can learn linearly non-separable exclusive-or (xor) problem under 1V supply with only 3.8μW of power consumption. Long-term, multi-stage synaptic memory contains more information for a longer time in a single synapse. 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The circuit is implemented using TSMC 0.18μm CMOS technology. Simulation results show that the circuit can learn linearly non-separable exclusive-or (xor) problem under 1V supply with only 3.8μW of power consumption. Long-term, multi-stage synaptic memory contains more information for a longer time in a single synapse. Comparison of the proposed PSNN with recent hardware neural networks is also provided.</description><subject>Artificial neural networks</subject><subject>Biological neural networks</subject><subject>Neurons</subject><subject>Power demand</subject><subject>Probabilistic logic</subject><subject>Training</subject><subject>Very large scale integration</subject><issn>2163-4025</issn><issn>2766-4465</issn><isbn>9781479914715</isbn><isbn>1479914711</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotUMFOwzAUCwgkptEv2CUfQEZekr4kxzHBQJrEAThwmtIu3cLapkqLqv09ldjFtizbBxOyAL4E4PbxKcT16mMpOMglorYo8IpkVhtQ2toJIL8mM6ERmVKY30waUDLFRX5Hsr7_4ZwDGmE4zsj3qqWxZeUxdLT2LrWhPTzQOo6si6NPtEuxcEWoQz-EkvZdOE0B2vrf5OqJhjGmEx3DcJw67YENPjW08U1M53tyW7m699mF5-Tr5flz_cq275u39WrLAuh8YKVCo0vplVPGVUoZmTtjDMJ-L0ohoBCurGxlQBsOXGoteIFicix64R3IOVn87wbv_a5LoXHpvLscI_8A1lRV_w</recordid><startdate>201310</startdate><enddate>201310</enddate><creator>Hung-Yi Hsieh</creator><creator>Kea-Tiong Tang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201310</creationdate><title>An on-chip learning, low-power probabilistic spiking neural network with long-term memory</title><author>Hung-Yi Hsieh ; Kea-Tiong Tang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c4687c3e4a48af44835a88861dd2c221b2acf9f817801037720b62f9f96e2ea13</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Artificial neural networks</topic><topic>Biological neural networks</topic><topic>Neurons</topic><topic>Power demand</topic><topic>Probabilistic logic</topic><topic>Training</topic><topic>Very large scale integration</topic><toplevel>online_resources</toplevel><creatorcontrib>Hung-Yi Hsieh</creatorcontrib><creatorcontrib>Kea-Tiong Tang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hung-Yi Hsieh</au><au>Kea-Tiong Tang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An on-chip learning, low-power probabilistic spiking neural network with long-term memory</atitle><btitle>2013 IEEE Biomedical Circuits and Systems Conference (BioCAS)</btitle><stitle>BioCAS</stitle><date>2013-10</date><risdate>2013</risdate><spage>5</spage><epage>8</epage><pages>5-8</pages><issn>2163-4025</issn><eissn>2766-4465</eissn><eisbn>9781479914715</eisbn><eisbn>1479914711</eisbn><abstract>This paper describes an analog probabilistic spiking neural network (PSNN) circuit for portable and implanted applications which especially require low power, small area and on-chip learning to ensure good mobility, body safety and continually accurate classification. The circuit is implemented using TSMC 0.18μm CMOS technology. Simulation results show that the circuit can learn linearly non-separable exclusive-or (xor) problem under 1V supply with only 3.8μW of power consumption. Long-term, multi-stage synaptic memory contains more information for a longer time in a single synapse. Comparison of the proposed PSNN with recent hardware neural networks is also provided.</abstract><pub>IEEE</pub><doi>10.1109/BioCAS.2013.6679626</doi><tpages>4</tpages></addata></record> |
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| identifier | ISSN: 2163-4025 |
| ispartof | 2013 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2013, p.5-8 |
| issn | 2163-4025 2766-4465 |
| language | eng |
| recordid | cdi_ieee_primary_6679626 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Biological neural networks Neurons Power demand Probabilistic logic Training Very large scale integration |
| title | An on-chip learning, low-power probabilistic spiking neural network with long-term memory |
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