A minimal-state processing search algorithm for satisfiability problems

The satisfiability problem (SAT) is a typical NP-complete problem where a wide range of applications has been studied. Given a set of variables U and a set of clauses C, the goal of SAT is to find a truth assignment to variables in U such that every clause in C is satisfied if it exits, or to derive the infeasibility otherwise. This paper presents an approximation algorithm, called a minimal-state processing search algorithm for SAT (MIPS-SAT). MIPS-SAT repeatedly transits minimal states in terms of the cost function for searching a solution through a construction stage and a refinement stage. The first stage greedily generates an initial state composed of as many satisfied clauses as possible. The second stage iteratively seeks a solution while keeping state minimality. The performance of MIPS-SAT is verified through solving DIMACS benchmark instances</p

A global routing technique for wave-steered design methodology

Wave-Steering is a new circuit design methodology to realize high throughput circuits by embedding layout friendly structures in silicon. Latches guarantee correct signal arrival times at the input of synthesized modules and maintain the high throughput of operation. This paper presents a global routing technique for networks of wave-steered blocks. Latches can be distributed along interconnects. Their number depends on net topologies and signal ordering at the inputs of wave steered blocks. here, we route nets using Steiner tree heuristics and determine signal ordering and latch positions on interconnect. The problem of total latch number minimization is solved using SAT formulation. Experimental results on benchmark circuits show the efficiency of our technique. We achieve on average a 40% latch reduction at minimum latency over un-optimized circuits operating at 250 MHz in 0.25 &#956;m CMOS technology</p

A minimal-state processing search algorithm for satisfiability problems

The satisfiability problem (SAT) is a typical NP-complete problem where a wide range of applications has been studied. Given a set of variables U and a set of clauses C, the goal of SAT is to find a truth assignment to variables in U such that every clause in C is satisfied if it exits, or to derive the infeasibility otherwise. This paper presents an approximation algorithm, called a minimal-state processing search algorithm for SAT (MIPS-SAT). MIPS-SAT repeatedly transits minimal states in terms of the cost function for searching a solution through a construction stage and a refinement stage. The first stage greedily generates an initial state composed of as many satisfied clauses as possible. The second stage iteratively seeks a solution while keeping state minimality. The performance of MIPS-SAT is verified through solving DIMACS benchmark instances</p