@article{928577,
      author = {Kang, Mingu. and Gonugondla, Sujan. and Shanbhag, Naresh  R.,},
      url = {http://library.usi.edu/record/928577},
      title = {Deep in-memory architectures for machine learning /},
      publisher = {Springer,},
      abstract = {This 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.},
      recid = {928577},
      pages = {1 online resource},
      address = {Cham :},
      year = {2020},
}