Narjes Rohani, Kobi Gal, Michael Gallagher, Areti Manataki
Providing insights into health data science education through artificial intelligence
BMC medical education
data flow natural paradigm describing dsp applications concurrent implementation parallel hardware data flow programs signal processing directed graphs node represents function arc represents signal path synchronous data flow sdf special case data flow either atomic large grain number data samples produced consumed node invocation specified priori nodes scheduled statically compile time onto single parallel programmable processors runtime overhead usually associated data flow evaporates multiple sample rates within system easily naturally handled conditions correctness sdf graph explained scheduling algorithms described homogeneous parallel processors sharing memory preliminary sdf software system automatically generating assembly language code dsp microcomputers described two new efficiency techniques introduced static buffering extension sdf efficiently implement conditionals