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000928577 020__ $$a9783030359713$$q(electronic book)
000928577 020__ $$a3030359719$$q(electronic book)
000928577 020__ $$z3030359700
000928577 020__ $$z9783030359706
000928577 035__ $$aSP(OCoLC)on1139220893
000928577 035__ $$aSP(OCoLC)1139220893
000928577 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dESU$$dOCLCF
000928577 049__ $$aISEA
000928577 050_4 $$aTK7895.M4
000928577 08204 $$a004.5$$223
000928577 1001_ $$aKang, Mingu.
000928577 24510 $$aDeep in-memory architectures for machine learning /$$cMingu Kang, Sujan Gonugondla, Naresh R. Shanbhag.
000928577 260__ $$aCham :$$bSpringer,$$c2020.
000928577 300__ $$a1 online resource
000928577 336__ $$atext$$btxt$$2rdacontent
000928577 337__ $$acomputer$$bc$$2rdamedia
000928577 338__ $$aonline resource$$bcr$$2rdacarrier
000928577 504__ $$aIncludes bibliographical references and index.
000928577 5050_ $$aIntroduction -- The Deep In-memory Architecture (DIMA) -- DIMA Prototype Integrated Circuits -- A Variation-Tolerant DIMA via On-Chip Training -- Mapping Inference Algorithms to DIMA -- PROMISE: A DIMA-based Accelerator -- Future Prospects -- Index.
000928577 506__ $$aAccess limited to authorized users.
000928577 520__ $$aThis book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy-latency-accuracy trade-offs. From first principles to lab prototypes, this book provides a comprehensive view of this emerging topic for both the practicing engineer in industry and the researcher in academia. The book is a journey into the exciting world of AI systems in hardware. Describes deep in-memory architectures for AI systems from first principles, covering both circuit design and architectures; Discusses how DIMAs pushes the limits of energy-delay product of decision-making machines via its intrinsic energy-SNR trade-off; Offers readers a unique Shannon-inspired perspective to understand the system-level energy-accuracy trade-off and robustness in such architectures; Illustrates principles and design methods via case studies of actual integrated circuit prototypes with measured results in the laboratory; Presents DIMA's various models to evaluate DIMA's decision-making accuracy, energy, and latency trade-offs with various design parameter.
000928577 650_0 $$aComputer storage devices.
000928577 650_0 $$aMachine learning.
000928577 7001_ $$aGonugondla, Sujan.
000928577 7001_ $$aShanbhag, Naresh R.,$$d1966-
000928577 77608 $$iPrint version: $$z3030359700$$z9783030359706$$w(OCoLC)1126218657
000928577 852__ $$bebk
000928577 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-35971-3$$zOnline Access$$91397441.1
000928577 909CO $$ooai:library.usi.edu:928577$$pGLOBAL_SET
000928577 980__ $$aEBOOK
000928577 980__ $$aBIB
000928577 982__ $$aEbook
000928577 983__ $$aOnline
000928577 994__ $$a92$$bISE