BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Toronto BEGIN:DAYLIGHT DTSTART:20220313T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211107T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20220121T151738Z UID:996ABDF5-A3F9-46E1-9A7F-FFB78615C560 DTSTART;TZID=America/Toronto:20211201T130000 DTEND;TZID=America/Toronto:20211201T234600 DESCRIPTION:Abstract: Data stored in the cloud or on mobile devices reside in physical memory systems with finite sizes. Today\, huge amounts of anal og data\, e.g.\, images\, sounds and videos\, are first converted into bin ary digital form and then information compression algorithms (source codin g\, e.g. the JPEG standard) and error-correcting codes (channel coding) ar e separately employed to minimize the amount of physical storage required. \n\nIn this talk\, I will present a new concept for directly storing analo g data in a compressed format into analog-valued memory. This new concept provides more efficient storage of analog data by combining the use of joi nt source channel coding (JSCC) from information theory with the use of em erging non-volatile memories (e.g.\, Phase-change Memory (PCM) and Resisti ve RAM (RRAM)) that can directly store analog values as continuously tuned physics properties (e.g.\, resistances). Specifically\, I will describe h ow we develop an adaptive JSCC scheme with neural network for lossy image compression and storage in analog-valued non-volatile memory arrays. Our e xperiments using PCM and RRAM array chips show competitive performance for storing analog data using this concept. I will also show JSCC provides re silience to the PCM and RRAM device technology non-idealities\, including defective cells\, device variability\, resistance drift\, and relaxation. We ho`pe this work can open up new opportunities for addressing pressing d emands for the storage of analog data.\n\nSpeaker bio: Xin Zheng is a fina l-year PhD student in Electrical Engineering at Stanford University\, advi sed by Prof. H. -S. Philip Wong. She received her B.S. degree in Physics f rom Nanjing University\, and her M.S. degree in Electrical Engineering fro m Stanford University. Her PhD research focuses on analog storage system e nabled by emerging memory technologies (e.g.\, RRAM and PCM)\, with techni cal efforts spanning from memory device characterization and modeling\, me mory array chip design and tape-out\, to analog signal coding scheme devel opment.\n\nCo-sponsored by: Waterloo Emerging Integrated System Group\, Un iversity of Waterloo\n\nSpeaker(s): Xin Zheng\, \n\nWaterloo\, Ontario\, C anada\, Virtual: https://events.vtools.ieee.org/m/291919 LOCATION:Waterloo\, Ontario\, Canada\, Virtual: https://events.vtools.ieee. org/m/291919 ORGANIZER:lan.wei@uwaterloo.ca SEQUENCE:2 SUMMARY:Women-in-Engineering Seminar Series - Seminar 1 URL;VALUE=URI:https://events.vtools.ieee.org/m/291919 X-ALT-DESC:Description: <br /><p><strong>Abstract:</strong> Data stored in the cloud or on mobile devices reside in physical memory systems with fini te sizes. Today\, huge amounts of analog data\, e.g.\, images\, sounds and videos\, are first converted into binary digital form and then informatio n compression algorithms (source coding\, e.g. the JPEG standard) and erro r-correcting codes (channel coding) are separately employed to minimize th e amount of physical storage required.</p>\n<p>In this talk\, I will prese nt a new concept for directly storing analog data in a compressed format i nto analog-valued memory. This new concept provides more efficient storage of analog data by combining the use of joint source channel coding (JSCC) from information theory with the use of emerging non-volatile memories (e .g.\, Phase-change Memory (PCM) and Resistive RAM (RRAM)) that can directl y store analog values as continuously tuned physics properties (e.g.\, res istances). Specifically\, I will describe how we develop an adaptive JSCC scheme with neural network for lossy image compression and storage in anal og-valued non-volatile memory arrays. Our experiments using PCM and RRAM a rray chips show competitive performance for storing analog data using this concept. I will also show JSCC provides resilience to the PCM and RRAM de vice technology non-idealities\, including defective cells\, device variab ility\, resistance drift\, and relaxation. We ho`pe this work can open up new opportunities for addressing pressing demands for the storage of analo g data.</p>\n<p><strong>Speaker bio</strong>: Xin Zheng is a final-year Ph D student in Electrical Engineering at Stanford University\, advised by Pr of. H. -S. Philip Wong. She received her B.S. degree in Physics from Nanji ng University\, and her M.S. degree in Electrical Engineering from Stanfor d University. Her PhD research focuses on analog storage system enabled by emerging memory technologies (e.g.\, RRAM and PCM)\, with technical effor ts spanning from memory device characterization and modeling\, memory arra y chip design and tape-out\, to analog signal coding scheme development.</ p> END:VEVENT END:VCALENDAR