000 03446nam a22005535i 4500
001 978-3-030-35971-3
003 DE-He213
005 20220801214108.0
007 cr nn 008mamaa
008 200130s2020 sz | s |||| 0|eng d
020 _a9783030359713
_9978-3-030-35971-3
024 7 _a10.1007/978-3-030-35971-3
_2doi
050 4 _aTK7867-7867.5
072 7 _aTJFC
_2bicssc
072 7 _aTEC008010
_2bisacsh
072 7 _aTJFC
_2thema
082 0 4 _a621.3815
_223
100 1 _aKang, Mingu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_936272
245 1 0 _aDeep In-memory Architectures for Machine Learning
_h[electronic resource] /
_cby Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aX, 174 p. 104 illus., 65 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
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.
650 0 _aElectronic circuits.
_919581
650 0 _aCooperating objects (Computer systems).
_96195
650 0 _aMicroprocessors.
_936273
650 0 _aComputer architecture.
_93513
650 1 4 _aElectronic Circuits and Systems.
_936274
650 2 4 _aCyber-Physical Systems.
_932475
650 2 4 _aProcessor Architectures.
_936275
700 1 _aGonugondla, Sujan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_936276
700 1 _aShanbhag, Naresh R.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_936277
710 2 _aSpringerLink (Online service)
_936278
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030359706
776 0 8 _iPrinted edition:
_z9783030359720
776 0 8 _iPrinted edition:
_z9783030359737
856 4 0 _uhttps://doi.org/10.1007/978-3-030-35971-3
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c75950
_d75950