Title :
Hardware neural network accelerators
Author_Institution :
INRIA Saclay, Saclay, France
fDate :
Sept. 29 2013-Oct. 4 2013
Abstract :
Because of increasingly stringent energy constraints (e.g., Dark Silicon, there is a growing consensus in the community that we may be moving towards heterogeneous multi-core architectures, composed of a mix of cores and accelerators. Because our community is traditionally focused on general-purpose computing, we have been especially considering accelerator approaches such as GPUs and reconfigurable circuits. An attractive alternative is to investigate accelerators which are focused on a few key algorithms: key algorithms still mean broad application scope, but few algorithms enable energy efficient and cost-effective accelerators.
Keywords :
application specific integrated circuits; fault tolerant computing; graphics processing units; learning (artificial intelligence); multiprocessing systems; neural nets; parallel architectures; power aware computing; ASIC; GPU; NN circuit; PARSEC benchmarks; cost-effective accelerators; defect tolerance; energy efficiency; fault tolerance; faulty parts; general-purpose computing; hardware neural network accelerators; heterogeneous multicore architectures; machine-learning techniques; reconfigurable circuits; stochastic algorithms; stringent energy constraints; Artificial neural networks; Biological neural networks; Circuit faults; Communities; Computer architecture; Hardware;
Conference_Titel :
Hardware/Software Codesign and System Synthesis (CODES+ISSS), 2013 International Conference on
Conference_Location :
Montreal, QC
DOI :
10.1109/CODES-ISSS.2013.6659008