Code density optimization for embedded DSP processors using data compression techniques

Code-size minimization in embedded systems is an important problem because code size directly affects production cost. We address the problem of code compression in systems with embedded DSP processors. We use data-compression methods to develop code-size minimization strategies. In our framework, the compressed program consists of a skeleton and a dictionary. We show that the dictionary can be computed by solving a set-covering problem derived from the original program. We also address performance considerations, and show that they can be incorporated easily into the set-covering formulation. Experimental results are presented.