000 02290nam a2200265 a 4500
001 vtls000081345
003 KUKTEM
005 20251125100058.0
008 140617t2014 nyua f 001 0 eng d
020 _a9781461477976 (hbk.)
039 9 _a201506181412
_baida
_y201406171400
_zezzatul
040 _aUMP
090 _aTK7885 .P38 2014
100 1 _aPaul, Somnath
245 1 0 _aComputing with memory for energy-efficient robust systems /
_cSomnath Paul, Swarup Bhunia
260 _aNew York :
_bSpringer,
_c2014
300 _axiii, 210 p. :
_bill. (some col.) ;
_c22 cm.
504 _aIncludes bibliographical references
520 _aThis book analyzes energy and reliability as major challenges faced by designers of computing frameworks in the nanometer technology regime. The authors describe the existing solutions to address these challenges and then reveal a new reconfigurable computing platform, which leverages high-density nanoscale memory for both data storage and computation to maximize the energy-efficiency and reliability. The energy and reliability benefits of this new paradigm are illustrated and the design challenges are discussed. Various hardware and software aspects of this exciting computing paradigm are described, particularly with respect to hardware-software co-designed frameworks, where the hardware unit can be reconfigured to mimic diverse application behavior. Finally, the energy-efficiency of the paradigm described is compared with other, well-known reconfigurable computing platforms. Introduces new paradigm for hardware reconfigurable frameworks, which leverages dense memory array as a malleable resource, which can be used for information storage as well as computation; Merges spatial and temporal computing to minimize interconnect overhead and achieve better scalability compared to state-of-the-art reconfigurable computing platforms; Enables efficient mapping of diverse data-intensive applications from domains of signal processing, multimedia and security applications
650 0 _aComputer engineering
650 0 _aComputers
_xEnergy consumption
650 0 _aNanoelectromechanical systems
700 1 _aBhunia, Swarup
999 _aVIRTUA40
_c77329
_d77335
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5040*5200*6500*6501*6502*7000*9992