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Our portfolio spans across multiple disciplines, including engineering, technology, medicine, and social sciences, providing cutting-edge insights for researchers, academics, and professionals. &nbsp;We work with experts and thought leaders to deliver timely, relevant, and peer-reviewed resources. 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Leveraging a broad portfolio of hardware, software and proprietary algorithms, Cirrus Logic excels at developing IP-rich solutions that create innovative user experiences for the world’s top mobile and consumer applications. Cirrus Logic has approximately 3,940 issued and pending patents worldwide and counting.\u003C/p>\u003Cp style=\"text-align:justify;\">Cirrus Logic is a recognized leader in low-power, high-precision, mixed-signal audio solutions that include products such as smart codecs, audio DSPs, boosted amplifiers, haptic and sensing ICs, camera controllers, battery and power ICs, analog-to-digital converters (“ADCs”), digital-to-analog converters (“DACs”), codecs that integrate ADCs and DACs into a single IC, digital interface ICs and power stages. Our products are used in a wide array of consumer applications, including smartphones, tablets, laptops, digital headsets, wearables, Bluetooth speakers, and smart home accessories.\u003C/p>","2024-12-18T13:52:37.376Z","2024-12-18T18:34:48.482Z","2024-12-18T13:52:39.678Z","320",{"id":1542,"name":1543,"alternativeText":16,"caption":16,"width":1544,"height":1545,"formats":1546,"hash":1569,"ext":19,"mime":20,"size":1570,"url":1571,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1572,"updatedAt":1572},249,"ISCAS_2025_Patron-Logos_cirrus-logic.png",1491,264,{"large":1547,"small":1553,"medium":1558,"thumbnail":1564},{"ext":19,"url":1548,"hash":1549,"mime":20,"name":1550,"path":16,"size":1551,"width":787,"height":1552},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/large_ISCAS_2025_Patron_Logos_cirrus_logic_7e45301a35.png","large_ISCAS_2025_Patron_Logos_cirrus_logic_7e45301a35","large_ISCAS_2025_Patron-Logos_cirrus-logic.png",35.81,177,{"ext":19,"url":1554,"hash":1555,"mime":20,"name":1556,"path":16,"size":1557,"width":736,"height":860},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/small_ISCAS_2025_Patron_Logos_cirrus_logic_7e45301a35.png","small_ISCAS_2025_Patron_Logos_cirrus_logic_7e45301a35","small_ISCAS_2025_Patron-Logos_cirrus-logic.png",15.47,{"ext":19,"url":1559,"hash":1560,"mime":20,"name":1561,"path":16,"size":1562,"width":743,"height":1563},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/medium_ISCAS_2025_Patron_Logos_cirrus_logic_7e45301a35.png","medium_ISCAS_2025_Patron_Logos_cirrus_logic_7e45301a35","medium_ISCAS_2025_Patron-Logos_cirrus-logic.png",25.11,133,{"ext":19,"url":1565,"hash":1566,"mime":20,"name":1567,"path":16,"size":1568,"width":805,"height":160},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/thumbnail_ISCAS_2025_Patron_Logos_cirrus_logic_7e45301a35.png","thumbnail_ISCAS_2025_Patron_Logos_cirrus_logic_7e45301a35","thumbnail_ISCAS_2025_Patron-Logos_cirrus-logic.png",6.7,"ISCAS_2025_Patron_Logos_cirrus_logic_7e45301a35",8.8,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/ISCAS_2025_Patron_Logos_cirrus_logic_7e45301a35.png","2024-12-18T13:52:21.065Z",{"id":1251,"variation":757,"button":1574},[1575],{"id":1576,"label":761,"size":762,"color":763,"style":16,"icon":764,"iconPosition":765,"url":1577,"newWindow":8,"downloadable":16,"shape":16},102,"http://cirrus.com/","-199",{"id":259,"name":1580,"description":1581,"createdAt":1582,"updatedAt":1583,"publishedAt":1584,"url_path_id":1585,"logo":1586,"website":1607,"url_path":1612},"Applied Materials","\u003Cp style=\"text-align:justify;\">We are the leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. Our expertise in modifying materials at atomic levels and on an industrial scale enables customers to transform possibilities into reality. At Applied Materials, our innovations Make Possible® a Better Future.\u003C/p>","2025-01-14T21:18:16.987Z","2025-01-14T21:18:19.761Z","2025-01-14T21:18:19.748Z","345",{"id":1587,"name":1588,"alternativeText":16,"caption":16,"width":1589,"height":1590,"formats":1591,"hash":1603,"ext":781,"mime":784,"size":1604,"url":1605,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1606,"updatedAt":1606},261,"AppliedWebLogoEnglish102021.jpg",525,187,{"small":1592,"thumbnail":1597},{"ext":781,"url":1593,"hash":1594,"mime":784,"name":1595,"path":16,"size":1596,"width":736,"height":1348},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/small_Applied_Web_Logo_English102021_cd0b2bb837.jpg","small_Applied_Web_Logo_English102021_cd0b2bb837","small_AppliedWebLogoEnglish102021.jpg",11.68,{"ext":781,"url":1598,"hash":1599,"mime":784,"name":1600,"path":16,"size":1601,"width":805,"height":1602},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/thumbnail_Applied_Web_Logo_English102021_cd0b2bb837.jpg","thumbnail_Applied_Web_Logo_English102021_cd0b2bb837","thumbnail_AppliedWebLogoEnglish102021.jpg",4.69,87,"Applied_Web_Logo_English102021_cd0b2bb837",11.64,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/Applied_Web_Logo_English102021_cd0b2bb837.jpg","2025-01-14T21:16:57.312Z",{"id":1334,"variation":757,"button":1608},[1609],{"id":1610,"label":761,"size":762,"color":763,"style":16,"icon":764,"iconPosition":765,"url":1611,"newWindow":8,"downloadable":16,"shape":16},103,"https://www.appliedmaterials.com/us/en.html","-221",{"id":280,"name":1614,"description":1615,"createdAt":1616,"updatedAt":1617,"publishedAt":1618,"url_path_id":1619,"logo":1620,"website":1629,"url_path":1635},"ASML","\u003Cp style=\"text-align:justify;\">As one of the world’s leading manufacturers of chip-making equipment, ASML provides its customers with tools - hardware, software and services - to create the patterns that define the electronic circuits on a chip. As we improve our products, our customers can increase the value and reduce the cost of chips for their customers.\u003C/p>\u003Cp style=\"text-align:justify;\">We are a global company with over 40,000 employees. Our thousands of engineers work in multi-disciplinary teams and with a network of suppliers and technology partners, innovating to maintain our technology leadership. We set ourselves ambitious goals and take pride in the impact we have on the world around us.\u003C/p>","2025-01-21T16:28:06.652Z","2025-01-21T16:28:08.471Z","2025-01-21T16:28:08.464Z","346",{"id":1621,"name":1622,"alternativeText":16,"caption":16,"width":1623,"height":1624,"formats":16,"hash":1625,"ext":33,"mime":34,"size":1626,"url":1627,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1628,"updatedAt":1628},266,"ASML_Holding_N.V._logo (1).svg",1065,300,"ASML_Holding_N_V_logo_1_85c2099fcf",3.33,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/ASML_Holding_N_V_logo_1_85c2099fcf.svg","2025-01-21T16:28:04.069Z",{"id":1630,"variation":757,"button":1631},86,[1632],{"id":1633,"label":761,"size":762,"color":763,"style":16,"icon":764,"iconPosition":765,"url":1634,"newWindow":8,"downloadable":16,"shape":16},104,"https://www.asml.com/","-222",{"pagination":1637},{"page":5,"pageSize":280,"pageCount":47,"total":554},{"id":280,"heading":281,"pageHeader":1639,"sections":1641},{"id":280,"description":1640,"showPageHeader":8,"backgroundColor":49,"image":16}," ",[1642,1647],{"id":395,"__component":1643,"componentVariation":1644,"styles":16,"header":16,"body":1645},"content.content","Content Image Left",{"id":1287,"title":16,"body":1646,"containerWidth":16,"buttonGroup":16,"media":16},"\u003Ch4 class=\"h4\" style=\"margin-left:0px;text-align:justify;\">\u003Cspan style=\"color:#8A2432;\">\u003Cstrong>Wednesday | May 28, 2025 | 11:30 - 13:00\u003C/strong>\u003C/span>\u003C/h4>\u003Cp>This exclusive panel session delves into urgent and important subjects related to brain-inspired and neural systems. The event offers a distinctive platform for distinguished scientists and engineers from both industry and academia to engage in an interdisciplinary dialogue on the crucial subject of energy-efficient operations in machines and biological brains. Following short presentations, the floor will be open for questions from the audience and further discussion.\u003C/p>\u003Cp>\u003Cstrong>Goal: \u003C/strong>Bring together engineers and neuroscientists to share insights related to energy-aware learning in Biology and Machine to foster cross-disciplinary interaction and enhance the understanding of brain-inspired computation.\u003C/p>\u003Cp>\u003Cstrong>Covered Topics:\u003C/strong>\u003C/p>\u003Cul>\u003Cli>Bio-inspiration and breakthroughs required for In-memory computing (IMC) to become a transformative technology for deep learning\u003C/li>\u003Cli>Insights from artificial and biological neural networks for edge intelligence\u003C/li>\u003Cli>The thermodynamics of sentience, free energy principle, achieving lower limit on thermodynamic work through minimization of computational complexity\u003C/li>\u003Cli>Metabolic cost of learning, comparison to computation models, energy saving strategies for plasticity\u003C/li>\u003Cli>Concepts of locality and sparsity to reduce energy footprint; exploitation of analog RRAMs for efficient computation\u003C/li>\u003Cli>Event-driven computation and SpiNNaker system examples, exploitation of spatio-temporal sparsity inherent in biological neural systems\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Session Chairs:\u003C/strong> Ali Muhtaroglu, Bipin Rajendran, Gert Cauwenberghs, Mark Van Rossum\u003C/p>\u003Cp>\u003Cstrong>Format:\u003C/strong>\u003C/p>\u003Cul>\u003Cli>First Round: Delivery of a 10-minute introductory presentation (pitch) on one of the above topics. The topic abstract is attached for each presenter in the table. Each panelist will be introduced by one of the session chairs proposing the session. The panelist presentations will be followed by a 10-minute break.\u003C/li>\u003Cli>Second Round: The floor will be opened to audience questions to panelists and panelist responses. The discussion will be led by one of the session chairs.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Rational: &nbsp;Why the topic is novel, why it is relevant to the ISCAS community, and how it fits within the innovation themes?\u003C/strong>\u003C/p>\u003Cp style=\"text-align:justify;\">The proposed session on Birds-of-a-Feather Panel Session in Energy-Aware Neural Engineering is both novel and timely, given the major thrust in emerging categories of brain-inspired architectures, deep-learning technologies, energy efficient machine learning systems and neuromorphic ICs in order to integrate intelligence and autonomy into exploding applications such as healthcare and rapid diagnostics, internet of everything, autonomous systems, robotics, vehicles and wearables. Hence, the session not only aligns with ISCAS 2025 innovation themes, but also provides a venue for distinguished scientists and industry experts active in this field to provide their perspective in an interactive and cross-disciplinary format. The proposing committee has prioritized discipline, topic and gender diversity in inviting the panelists. As part of the novelty of the panel, prominent neuroscientists will be included in the discussion on how engineers can be further inspired by the brain in understanding and optimizing energy consumed in learning processes.\u003Cbr>&nbsp;\u003C/p>",{"id":89,"__component":1648,"componentVariation":1649,"contactsVariation":1650,"styles":16,"header":16,"sessionsGroup":1651},"content.sessions","Sessions Base","Card Contact Full",[1652],{"id":89,"groupTitle":16,"sessions":1653},[1654,1692,1743,1794,1844,1881],{"id":1338,"session":1655},{"id":1360,"title":1656,"teaser":1657,"body":1658,"createdAt":1659,"updatedAt":1660,"publishedAt":1661,"url_path_id":1662,"contacts":1663,"url_path":1691},"Can in-memory computing ever become a transformative technology for deep learning?","\u003Cp style=\"text-align:justify;\">In-memory computing (IMC) is an emerging paradigm that addresses the processor-memory dichotomy in modern computing systems, particularly suited for deep neural networks (DNNs). IMC leverages memory devices' physical attributes and array-level organization for synaptic operations, often involving analogue computation and emerging memory devices. This approach is inspired by the computational principles observed in the human brain, such as hard-wired neural networks and analogue processing. I would like to share my thoughts on where the field stands and what additional bio-inspiration and breakthroughs are required for IMC to become a transformative technology for deep learning.&nbsp;\u003C/p>","\u003Cp style=\"text-align:justify;\">In-memory computing (IMC) is an emerging paradigm that addresses the processor-memory dichotomy in modern computing systems, particularly suited for deep learning acceleration. IMC leverages memory devices' physical attributes and array-level organization for synaptic operations, often involving analogue computation and emerging memory devices. This approach is inspired by the computational principles observed in the human brain, such as hard-wired neural networks and analogue processing.&nbsp;\u003C/p>\u003Cp style=\"text-align:justify;\">IMC based on static random-access memory (SRAM) is mature enough for near-term commercialization. However, a significant limitation is that SRAM-based arrays lack the density to store all the weights of most deep neural networks (DNNs) on a single die within practical chip area constraints. A potential solution is to transition to non-volatile memory technologies, such as memristive systems (e.g., Resistive RAM (ReRAM), Phase Change Memory (PCM)) and Flash memory, which offer much higher memory density compared to SRAM. Additionally, these emerging memories provide non-volatile storage, allowing accelerator chips to power cycle without needing to reload weights.\u003Cbr>Despite these advantages, current IMC implementations remain limited and are likely to serve niche edge applications. While the concept of in-place computing with physically implemented synaptic weights is promising, it alone is insufficient. To unlock its full potential, we need to significantly enhance weight storage capacity, reduce communication latency, and support additional computational primitives. I would like to share my thoughts on what additional bio-inspiration and breakthroughs are required for IMC to become a transformative technology for deep learning.\u003C/p>","2024-12-20T16:09:37.046Z","2024-12-20T16:09:38.723Z","2024-12-20T16:09:38.716Z","328",[1664],{"id":1665,"name":1666,"committee":16,"position":16,"affiliation":1667,"email":16,"biography":1668,"createdAt":1669,"updatedAt":1670,"url_path_id":1671,"contactPhoto":1672,"socialLinks":1685,"url_path":1690},215,"Abu Sebastian","IBM Research-Zurich, Switzerland","\u003Cp>Dr. Abu Sebastian is a Distinguished Scientist and technical manager at IBM Research – Zurich. In 2015 he was awarded the European Research Council (ERC) consolidator grant and in 2020, he was awarded an ERC Proof-of-concept grant. He was an IBM Master Inventor and was named Principal and Distinguished Research Staff Member in 2018 and 2020, respectively. In 2019, he received the Ovshinsky Lectureship Award for his contributions to \"Phase-change materials for cognitive computing\". In 2023, he was conferred the title of Visiting Professor in Materials by University of Oxford. He is a distinguished lecturer and fellow of the IEEE.\u003C/p>","2024-12-20T16:03:12.177Z","2025-02-19T14:35:02.159Z","323",{"id":1673,"name":1674,"alternativeText":16,"caption":16,"width":1496,"height":1496,"formats":1675,"hash":1681,"ext":781,"mime":784,"size":1682,"url":1683,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1684,"updatedAt":1684},250,"Abu Sebastian.jpg",{"thumbnail":1676},{"ext":781,"url":1677,"hash":1678,"mime":784,"name":1679,"path":16,"size":1680,"width":751,"height":751},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/thumbnail_Abu_Sebastian_74be26f4d1.jpg","thumbnail_Abu_Sebastian_74be26f4d1","thumbnail_Abu Sebastian.jpg",4.96,"Abu_Sebastian_74be26f4d1",11.78,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/Abu_Sebastian_74be26f4d1.jpg","2024-12-20T16:02:49.070Z",[1686],{"id":1687,"url":1688,"platform":1689},692,"https://research.ibm.com/people/abu-sebastian","Website","-201","-200",{"id":1360,"session":1693},{"id":1372,"title":1694,"teaser":1695,"body":44,"createdAt":1696,"updatedAt":1697,"publishedAt":1698,"url_path_id":1699,"contacts":1700,"url_path":1742},"Merging insights from artificial and biological neural networks for neuromorphic edge intelligence","\u003Cp style=\"text-align:justify;\">The development of efficient bio-inspired algorithms and hardware is currently missing a clear framework. Should we start from the brain computational primitives and figure out how to apply them to real-world problems (bottom-up approach), or should we build on working AI solutions and fine-tune them to increase their biological plausibility (top-down approach)? We will see why biological plausibility and hardware efficiency are often two sides of the same coin, and how neuroscience- and AI-driven insights can cross-feed each other toward neuromorphic edge intelligence. By applying these findings to real-world problems such as on-device learning and safety-critical scenarios, we will show how smart devices can be made to adapt to their environment and users within a few microwatts, or how they can be made to react to stimulus within just a few microseconds.\u003C/p>","2024-12-20T16:10:03.267Z","2024-12-20T16:10:06.261Z","2024-12-20T16:10:06.254Z","329",[1701],{"id":1702,"name":1703,"committee":16,"position":16,"affiliation":1704,"email":16,"biography":1705,"createdAt":1706,"updatedAt":1707,"url_path_id":1708,"contactPhoto":1709,"socialLinks":1737,"url_path":1741},216,"Charlotte Frenkel","TU Delft, Netherlands","\u003Cp style=\"text-align:justify;\">Charlotte Frenkel received the M.Sc. degree (summa cum laude) in Electromechanical Engineering and the Ph.D. degree in Engineering Science from Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium, in 2015 and 2020, respectively. In February 2020, she joined the Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland, as a postdoctoral researcher. In July 2022, she started as an assistant professor in the Electronic Instrumentation group at Delft University of Technology. Her current research aims at bridging the bottom-up (bio-inspired) and top-down (engineering-driven) design approaches toward neuromorphic intelligence, with a focus on digital and mixed-signal spiking neural network processor design, embedded machine learning, and on-chip training algorithms. She presented several invited talks, including keynotes at the tinyML EMEA technical forum 2021, at the NICE neuromorphic conference 2021, and at Neuromorphic Computing Netherlands (NCN) 2022.\u003C/p>","2024-12-20T16:04:22.190Z","2024-12-20T16:04:33.639Z","324",{"id":479,"name":1710,"alternativeText":16,"caption":16,"width":1711,"height":1711,"formats":1712,"hash":1733,"ext":19,"mime":20,"size":1734,"url":1735,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1736,"updatedAt":1736},"Untitled design - 2024-12-20T110413.404.png",1080,{"large":1713,"small":1718,"medium":1723,"thumbnail":1728},{"ext":19,"url":1714,"hash":1715,"mime":20,"name":1716,"path":16,"size":1717,"width":787,"height":787},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/large_Untitled_design_2024_12_20_T110413_404_a7ed6d1834.png","large_Untitled_design_2024_12_20_T110413_404_a7ed6d1834","large_Untitled design - 2024-12-20T110413.404.png",1237.07,{"ext":19,"url":1719,"hash":1720,"mime":20,"name":1721,"path":16,"size":1722,"width":736,"height":736},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/small_Untitled_design_2024_12_20_T110413_404_a7ed6d1834.png","small_Untitled_design_2024_12_20_T110413_404_a7ed6d1834","small_Untitled design - 2024-12-20T110413.404.png",373.28,{"ext":19,"url":1724,"hash":1725,"mime":20,"name":1726,"path":16,"size":1727,"width":743,"height":743},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/medium_Untitled_design_2024_12_20_T110413_404_a7ed6d1834.png","medium_Untitled_design_2024_12_20_T110413_404_a7ed6d1834","medium_Untitled design - 2024-12-20T110413.404.png",763.31,{"ext":19,"url":1729,"hash":1730,"mime":20,"name":1731,"path":16,"size":1732,"width":751,"height":751},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/thumbnail_Untitled_design_2024_12_20_T110413_404_a7ed6d1834.png","thumbnail_Untitled_design_2024_12_20_T110413_404_a7ed6d1834","thumbnail_Untitled design - 2024-12-20T110413.404.png",44.99,"Untitled_design_2024_12_20_T110413_404_a7ed6d1834",290.15,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/Untitled_design_2024_12_20_T110413_404_a7ed6d1834.png","2024-12-20T16:04:19.523Z",[1738],{"id":1739,"url":1740,"platform":1689},693,"https://www.tudelft.nl/ewi/cogsys/people/charlotte-frenkel","-202","-206",{"id":1372,"session":1744},{"id":1406,"title":1745,"teaser":1746,"body":1747,"createdAt":1748,"updatedAt":1749,"publishedAt":1750,"url_path_id":1751,"contacts":1752,"url_path":1793},"The thermodynamics of sentience","\u003Cp style=\"text-align:justify;\">What are the principles that underwrite sentient behaviour? This presentation uses the free energy principle to furnish an account in terms of active inference. The free energy principle formulates data assimilation and decision-making from the point of view of physics; in particular, the properties that self-organising systems—that distinguish themselves from their world—must possess. The narrative starts with a heuristic proof suggesting that life—or self-organization to nonequilibrium steady-states—is an emergent property of any dynamical system that possesses a Markov blanket. Crucially, a Markov blanket equips the system with a particular Lagrangian called variational free energy.&nbsp;\u003C/p>","\u003Cp style=\"text-align:justify;\">This free energy is the same quantity that is optimized in Bayesian inference and machine learning (where it is known as an evidence lower bound). In brief, this means that any system can be read as minimising variational free energy or maximising evidence (a.k.a., marginal likelihood). Crucially, variational free energy can be expressed as accuracy and complexity. Therefore, to be Bayes optimal is to minimise computational complexity which, affords a lower (Landauer) limit on thermodynamic work. To attain this limit, means eluding the van Neumann bottleneck (a.k.a., memory wall) with in memory processing, along the lines of mortal computation or neuromorphic computing. This analysis suggests that any system — or ecosystem — in exchange with another can be read in terms of inferring the state of the other system and acting accordingly under constraints afforded by the kind of system it is. Crucially, this exchange among open systems can be simulated, emulated or reproduced in silico to provide biomimetic efficiency in both thermodynamic and computational terms if, and only if, the system conforms with the principles of mortal computation; namely, computation is substrate dependent and features — from a computer science perspective — things like reactive message passing and in memory processing. Under these conditions, the Bayes optimality of implicit information processing translates into thermodynamic efficiency via the Jarzynski equality, which places a bound on the thermodynamic work required for Bayesian belief updating.\u003C/p>","2024-12-20T16:10:46.676Z","2024-12-20T16:10:49.642Z","2024-12-20T16:10:49.635Z","330",[1753],{"id":1754,"name":1755,"committee":16,"position":16,"affiliation":1756,"email":16,"biography":1757,"createdAt":1758,"updatedAt":1758,"url_path_id":1759,"contactPhoto":1760,"socialLinks":1788,"url_path":1792},217,"Karl Friston","University College London, Imaging Neuroscience, UK","\u003Cp style=\"text-align:justify;\">Karl Friston is a theoretical neuroscientist and authority on brain imaging. He invented statistical parametric mapping (SPM), voxel-based morphometry (VBM) and dynamic causal modelling (DCM). These contributions were motivated by schizophrenia research and theoretical studies of value-learning, formulated as the dysconnection hypothesis of schizophrenia. Mathematical contributions include variational Laplacian procedures and generalized filtering for hierarchical Bayesian model inversion. Friston currently works on models of functional integration in the human brain and the principles that underlie neuronal interactions. His main contribution to theoretical neurobiology is a free-energy principle for action and perception (active inference). Friston received the first Young Investigators Award in Human Brain Mapping (1996) and was elected a Fellow of the Academy of Medical Sciences (1999). In 2000 he was President of the international Organization of Human Brain Mapping. In 2003 he was awarded the Minerva Golden Brain Award and was elected a Fellow of the Royal Society in 2006. In 2008 he received a Medal, College de France and an Honorary Doctorate from the University of York in 2011. He became of Fellow of the Royal Society of Biology in 2012, received the Weldon Memorial prize and Medal in 2013 for contributions to mathematical biology and was elected as a member of EMBO (excellence in the life sciences) in 2014 and the Academia Europaea in (2015). He was the 2016 recipient of the Charles Branch Award for unparalleled breakthroughs in Brain Research and the Glass Brain Award, a lifetime achievement award in the field of human brain mapping. He holds Honorary Doctorates from the University of Zurich and Radboud University.\u003C/p>","2024-12-20T16:05:52.950Z","325",{"id":1761,"name":1762,"alternativeText":16,"caption":16,"width":1711,"height":1711,"formats":1763,"hash":1784,"ext":19,"mime":20,"size":1785,"url":1786,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1787,"updatedAt":1787},252,"Untitled design - 2024-12-20T110520.718.png",{"large":1764,"small":1769,"medium":1774,"thumbnail":1779},{"ext":19,"url":1765,"hash":1766,"mime":20,"name":1767,"path":16,"size":1768,"width":787,"height":787},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/large_Untitled_design_2024_12_20_T110520_718_b5ead12ace.png","large_Untitled_design_2024_12_20_T110520_718_b5ead12ace","large_Untitled design - 2024-12-20T110520.718.png",1310.03,{"ext":19,"url":1770,"hash":1771,"mime":20,"name":1772,"path":16,"size":1773,"width":736,"height":736},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/small_Untitled_design_2024_12_20_T110520_718_b5ead12ace.png","small_Untitled_design_2024_12_20_T110520_718_b5ead12ace","small_Untitled design - 2024-12-20T110520.718.png",384.9,{"ext":19,"url":1775,"hash":1776,"mime":20,"name":1777,"path":16,"size":1778,"width":743,"height":743},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/medium_Untitled_design_2024_12_20_T110520_718_b5ead12ace.png","medium_Untitled_design_2024_12_20_T110520_718_b5ead12ace","medium_Untitled design - 2024-12-20T110520.718.png",795.99,{"ext":19,"url":1780,"hash":1781,"mime":20,"name":1782,"path":16,"size":1783,"width":751,"height":751},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/thumbnail_Untitled_design_2024_12_20_T110520_718_b5ead12ace.png","thumbnail_Untitled_design_2024_12_20_T110520_718_b5ead12ace","thumbnail_Untitled design - 2024-12-20T110520.718.png",50.82,"Untitled_design_2024_12_20_T110520_718_b5ead12ace",317.97,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/Untitled_design_2024_12_20_T110520_718_b5ead12ace.png","2024-12-20T16:05:26.515Z",[1789],{"id":1790,"url":1791,"platform":1689},694,"https://profiles.ucl.ac.uk/2747-karl-friston","-203","-207",{"id":535,"session":1795},{"id":620,"title":1796,"teaser":1797,"body":44,"createdAt":1798,"updatedAt":1799,"publishedAt":1800,"url_path_id":1801,"contacts":1802,"url_path":1843},"Energy Efficient Learning in Neural Networks","\u003Cp style=\"text-align:justify;\">The brain is one of the most energy intense organs. Some of this energy is used for neural information processing, however, fruit fly experiments have also shown that learning is metabolically costly. First, we will present estimates of this cost, introduce a general model of this cost, and compare it to costs in computers. Next, we turn to a supervised artificial network setting and explore a number of strategies that can save energy needed for plasticity, either by modifying the cost function, by restricting plasticity, or by using less costly transient forms of plasticity. Finally, we will discuss adaptive strategies and possible relevance for biological learning.\u003C/p>","2025-01-02T13:59:31.226Z","2025-01-02T13:59:33.322Z","2025-01-02T13:59:33.316Z","334",[1803],{"id":1804,"name":1805,"committee":16,"position":16,"affiliation":1806,"email":16,"biography":1807,"createdAt":1808,"updatedAt":1808,"url_path_id":1809,"contactPhoto":1810,"socialLinks":1838,"url_path":1842},220,"Mark van Rossum","University of Nottingham, UK","\u003Cp style=\"text-align:justify;\">Professor Mark van Rossum heads the Neural Computation group at the University of Nottingham. Mark has a PhD in Physics from the University of Amsterdam and subsequently was did postdocs with Robert Smith at University of Pennsylvania and with Gina Turrigiano at Brandeis University. Before coming to Nottingham he lead the Doctoral Training Centre in Neuro-informatics at the University of Edinburgh. His research interests include synaptic plasticity and homeostasis, neural coding, and data analysis methods. He is best known for his work on STDP and the Van Rossum spike distance. His most recent work concerns the metabolic cost of plasticity.\u003C/p>","2025-01-02T13:58:45.760Z","333",{"id":1811,"name":1812,"alternativeText":16,"caption":16,"width":1711,"height":1711,"formats":1813,"hash":1834,"ext":19,"mime":20,"size":1835,"url":1836,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1837,"updatedAt":1837},255,"Untitled design - 2025-01-02T085712.944.png",{"large":1814,"small":1819,"medium":1824,"thumbnail":1829},{"ext":19,"url":1815,"hash":1816,"mime":20,"name":1817,"path":16,"size":1818,"width":787,"height":787},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/large_Untitled_design_2025_01_02_T085712_944_b2bf084422.png","large_Untitled_design_2025_01_02_T085712_944_b2bf084422","large_Untitled design - 2025-01-02T085712.944.png",1339.55,{"ext":19,"url":1820,"hash":1821,"mime":20,"name":1822,"path":16,"size":1823,"width":736,"height":736},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/small_Untitled_design_2025_01_02_T085712_944_b2bf084422.png","small_Untitled_design_2025_01_02_T085712_944_b2bf084422","small_Untitled design - 2025-01-02T085712.944.png",385.43,{"ext":19,"url":1825,"hash":1826,"mime":20,"name":1827,"path":16,"size":1828,"width":743,"height":743},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/medium_Untitled_design_2025_01_02_T085712_944_b2bf084422.png","medium_Untitled_design_2025_01_02_T085712_944_b2bf084422","medium_Untitled design - 2025-01-02T085712.944.png",806.17,{"ext":19,"url":1830,"hash":1831,"mime":20,"name":1832,"path":16,"size":1833,"width":751,"height":751},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/thumbnail_Untitled_design_2025_01_02_T085712_944_b2bf084422.png","thumbnail_Untitled_design_2025_01_02_T085712_944_b2bf084422","thumbnail_Untitled design - 2025-01-02T085712.944.png",49.05,"Untitled_design_2025_01_02_T085712_944_b2bf084422",317.4,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/Untitled_design_2025_01_02_T085712_944_b2bf084422.png","2025-01-02T13:57:19.254Z",[1839],{"id":1840,"url":1841,"platform":1689},697,"https://www.nottingham.ac.uk/psychology/people/mar k.vanrossum","-210","-211",{"id":1406,"session":1845},{"id":238,"title":1846,"teaser":1847,"body":1848,"createdAt":1849,"updatedAt":1850,"publishedAt":1851,"url_path_id":1852,"contacts":1853,"url_path":1880},"Analog substrates for temporal and local event-based computation","\u003Cp style=\"text-align:justify;\">The main source of energy consumption in neural network hardware is the amount of data being transferred and the distances it travels. Spiking Neural Network (SNN) hardware addresses this by encoding information sparsely in event timing, and localizing computation and memory, thereby minimizing data movement. Recent advancements in memory technologies, particularly Resistive Random-Access Memory (RRAM), have further enabled computation within memory, reducing energy overheads.\u003Cbr>&nbsp;\u003C/p>","\u003Cp style=\"text-align:justify;\">SNNs leverage temporal dynamics for computation, utilizing the timing of spikes to encode information. We explore how introducing delays in feedforward SNNs enhances their memory efficiency, and how RRAM-based In-Memory Computing (IMC) architectures can be augmented to incorporate such parameters. By embedding delays directly into the architecture, we achieve an order of magnitude of reduction in memory footprint, compared to an iso-accuracy Recurrent SNN. Moreover, the analog nature of RRAMs results in physical heterogeneity which we will exploit for hardware aware training of SNNs mappable to our architectures.\u003C/p>\u003Cp style=\"text-align:justify;\">As the scale of IMC arrays increases, multi-core architectures become critical. Energy efficiency in these systems will be mainly dominated by the communication network on chip. I will present how RRAM-based in-memory cores can also support energy-efficient in-memory routing (IMR). Based on this, we introduce a modular systolic array of IMC cores, alternated with IMR cores, which effectively implement a locally dense, globally sparse connectivity. This so-called “small world” connectivity is also the solution found in mammalian brains, minimizing the wiring costs.&nbsp;\u003C/p>\u003Cp style=\"text-align:justify;\">Finally, I will discuss how such specific on-chip routing with specific memory placement calls for a co-design approach with hardware-architecture and algorithm during network optimization. This requires a routing-aware training method which is often a non-differentiable function. I talk about possible solutions in this direction for an end-to-end optimized system with optimal memory usage.\u003C/p>","2024-12-20T16:11:52.901Z","2024-12-20T16:11:55.188Z","2024-12-20T16:11:55.171Z","331",[1854],{"id":1855,"name":1856,"committee":16,"position":16,"affiliation":1857,"email":16,"biography":1858,"createdAt":1859,"updatedAt":1859,"url_path_id":1860,"contactPhoto":1861,"socialLinks":1875,"url_path":1879},218,"Melika Payvand","University of Zurich and ETH Zurich, Switzerland","\u003Cp style=\"text-align:justify;\">Melika Payvand is an Assistant Professor at the Institute of Neuroinformatics, University of Zurich and ETH Zurich, where she leads the Emerging Intelligent Substrates lab. She received MSc and PhD degrees in Electrical and Computer Engineering from the University of California, Santa Barbara, in 2012 and 2016, respectively. Her research focuses on understanding natural intelligence and implementing its organizing principles in silico to develop efficient artificial intelligence (AI) systems. She has co-coordinated the European project NEUROTECH, co-chaired the International Conference on Neuromorphic Systems (ICONS), and co-organized the scientific program of the Capocaccia Neuromorphic Intelligence workshop from 2019 to 2023. She is also the chair-elect of the Neural Systems and Applications of the IEEE Circuits and Systems Society.\u003C/p>","2024-12-20T16:07:18.188Z","326",{"id":1862,"name":1863,"alternativeText":16,"caption":16,"width":1864,"height":1864,"formats":1865,"hash":1871,"ext":781,"mime":784,"size":1872,"url":1873,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1874,"updatedAt":1874},253,"Melika Payvand.jpg",227,{"thumbnail":1866},{"ext":781,"url":1867,"hash":1868,"mime":784,"name":1869,"path":16,"size":1870,"width":751,"height":751},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/thumbnail_Melika_Payvand_ccbe50b904.jpg","thumbnail_Melika_Payvand_ccbe50b904","thumbnail_Melika Payvand.jpg",5.33,"Melika_Payvand_ccbe50b904",9.77,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/Melika_Payvand_ccbe50b904.jpg","2024-12-20T16:06:56.245Z",[1876],{"id":1877,"url":1878,"platform":42},695,"https://www.linkedin.com/in/melika-payvand-a9897625/?originalSubdomain=ch","-204","-208",{"id":238,"session":1882},{"id":535,"title":1883,"teaser":1884,"body":1885,"createdAt":1886,"updatedAt":1887,"publishedAt":1888,"url_path_id":1889,"contacts":1890,"url_path":1930},"Event-driven computation in the SpiNNaker system","\u003Cp style=\"text-align:justify;\">The million-core SpiNNaker machine, developed at the University of Manchester primarily for brain modelling applications, has supported an open neuromorphic computing platform under the auspices of the EU Flagship Human Brain Project since 2016. The central feature of the machine includes a flexible multicast communication system that conveys neural spike information as small packets and an event-driven execution model that exploits the spatio-temporal sparsity inherent in biological neural systems.\u003Cbr>&nbsp;\u003C/p>","\u003Cp style=\"text-align:justify;\">Neurons in the brain communicate principally by issuing electro-chemical action potentials, or spikes. These spikes are regenerated as they propagate along the neuron’s axon, leading to the standard assumption that there is no information in the size or shape of the spike. The only information is in the timing of the spike and the identity of the neuron that spiked. Thus a spike can be viewed as a pure asynchronous event, leading to the Address Event Representation (AER) of neural activity, where complex spike patterns are captured as a time sequence of integers, each integer representing the address identity of a particular spiking neuron, and the timing of the integer capturing the time of the spike from that neuron.\u003C/p>\u003Cp style=\"text-align:justify;\">On SpiNNaker the AER data stream is conveyed over a packet-switched fabric, each packet carrying the information for one spike. Biological neurons are highly connected, for example each cortical neuron connects to many thousands of other neurons. This is supported in SpiNNaker through the use of multicast packet routing – a packet representing the spike from a single neuron propagates along a configurable tree pathway, being replicated as required at branch points, resulting in the delivery of as many packets as are required to the leaf nodes of the tree.\u003C/p>\u003Cp style=\"text-align:justify;\">The arrival of a spike packet at a leaf node is an event that causes activity at that node to handle the relevant synaptic stimulation of the neurons modelled there. The processor responsible for those neurons carries out the relevant calculations and then returns to a low-power idle state awaiting the next event. In this way, activity, and energy-consumption, is minimised to just that required to handle the incoming events.&nbsp;\u003C/p>","2024-12-20T16:12:30.421Z","2024-12-20T16:12:44.380Z","2024-12-20T16:12:32.620Z","332",[1891],{"id":908,"name":1892,"committee":16,"position":16,"affiliation":1893,"email":16,"biography":1894,"createdAt":1895,"updatedAt":1895,"url_path_id":1896,"contactPhoto":1897,"socialLinks":1925,"url_path":1929},"Steve Furber","University of Manchester, UK","\u003Cp style=\"text-align:justify;\">Steve Furber CBE FRS FREng, IEEE Fellow, is Professor Emeritus in the Department of Computer Science at the University of Manchester, UK. After completing a BA in mathematics and a PhD in aerodynamics at the University of Cambridge, UK, he spent the 1980s at Acorn Computers, where he was a principal designer of the BBC Microcomputer and the ARM 32-bit RISC microprocessor. Over 250 billion ARM-powered chips have since been manufactured, powering much of the world's mobile and embedded computing. He moved to the ICL Chair of Computer Engineering at Manchester in 1990 where he led research into asynchronous and low-power systems and, more recently, neural systems engineering, where the SpiNNaker project delivered a computer incorporating a million ARM processors optimised for brain modelling applications.\u003C/p>","2024-12-20T16:08:47.558Z","327",{"id":1898,"name":1899,"alternativeText":16,"caption":16,"width":1711,"height":1711,"formats":1900,"hash":1921,"ext":19,"mime":20,"size":1922,"url":1923,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1924,"updatedAt":1924},254,"Untitled design - 2024-12-20T110838.311.png",{"large":1901,"small":1906,"medium":1911,"thumbnail":1916},{"ext":19,"url":1902,"hash":1903,"mime":20,"name":1904,"path":16,"size":1905,"width":787,"height":787},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/large_Untitled_design_2024_12_20_T110838_311_aa2fdfaafb.png","large_Untitled_design_2024_12_20_T110838_311_aa2fdfaafb","large_Untitled design - 2024-12-20T110838.311.png",1541.81,{"ext":19,"url":1907,"hash":1908,"mime":20,"name":1909,"path":16,"size":1910,"width":736,"height":736},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/small_Untitled_design_2024_12_20_T110838_311_aa2fdfaafb.png","small_Untitled_design_2024_12_20_T110838_311_aa2fdfaafb","small_Untitled design - 2024-12-20T110838.311.png",455.03,{"ext":19,"url":1912,"hash":1913,"mime":20,"name":1914,"path":16,"size":1915,"width":743,"height":743},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/medium_Untitled_design_2024_12_20_T110838_311_aa2fdfaafb.png","medium_Untitled_design_2024_12_20_T110838_311_aa2fdfaafb","medium_Untitled design - 2024-12-20T110838.311.png",941.07,{"ext":19,"url":1917,"hash":1918,"mime":20,"name":1919,"path":16,"size":1920,"width":751,"height":751},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/thumbnail_Untitled_design_2024_12_20_T110838_311_aa2fdfaafb.png","thumbnail_Untitled_design_2024_12_20_T110838_311_aa2fdfaafb","thumbnail_Untitled design - 2024-12-20T110838.311.png",54.05,"Untitled_design_2024_12_20_T110838_311_aa2fdfaafb",354,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/iscas25/Untitled_design_2024_12_20_T110838_311_aa2fdfaafb.png","2024-12-20T16:08:44.558Z",[1926],{"id":1927,"url":1928,"platform":1689},696,"http://apt.cs.manchester.ac.uk/people/sfurber/","-205","-209",{"data":1932,"meta":1933},{"id":280,"heading":281,"createdAt":282,"updatedAt":283,"publishedAt":284,"url_path_id":285,"url_path":288,"contentType":78},{},1778852848907]