We describe a SAT-solver, BerkMin, that inherits such features of GRASP, SATO, and Chaff as clause recording, fast BCP, restarts, and conflict clause “aging”. At the same time BerkMin introduces a new decision making procedure and a new method of clause database management. We experimentally compare BerkMin with Chaff, the leader among SAT-solvers used in the EDA domain. Experiments show that our solver is more robust than Chaff. BerkMin solved all the instances we used in experiments including very large CNFs from a microprocessor verification benchmark suite. On the other hand, Chaff was not able to complete some instances even with the timeout limit of 16 hours. 1.

This paper describes a new method for exact hazard-free logic minimization of Boolean functions. Given an incompletely-specified Boolean function, the method produces a minimum-cost sum-ofproducts implementation which is hazard-free for a given set of multiple-input changes, if such a solution exists. The method is a constrained version of the Quine-McCluskey algorithm. It has been automated and applied to a number of examples. Results are compared with results of a comparable non-hazard-free method (espresso-exact). Overhead due to hazard-elimination is shown to be negligible.

As SAT-algorithms become more and more complex, there is little chance of writing a SAT-solver that is free of bugs. So it is of great importance to be able to verify the information returned by a SAT-solver. If the CNF formula to be tested is satisfiable, solution verification is trivial and can be easily done by the user. However, in the case of unsatisfiability, the user has to rely on the reputation of the SAT-solver. We describe an efficient procedure for checking the correctness of unsatisfiability proofs. As a by-product, the proposed procedure finds an unsatisfiable core of the initial CNF formula. The efficiency of the proposed procedure was tested on a representative set of large “real-life ” CNF formulas from the formal verification domain. 1.

In the paper, we consider the problem of checking whether cubes from a set S are implicants of a DNF formula D, at the same time minimizing the overall time taken by the checks. An obvious but inefficient way of solving the problem is to perform all the checks independently. In the paper, we consider a different approach. The key idea is that when checking whether a cube C from S is an implicant of D we can deduce (learn) implicants of D that are not implicants of C. These cubes can be used in the following checks for search pruning. Experiments on random DNF formulas, DIMACS benchmarks and DNF formulas describing circuits show that the proposed learning procedure reduces the overall time taken by checks by up to two orders of magnitude.

Datapath design is one of the most critical elements in the design of a high performance microprocessor. However datapath design is typically done manually, and is often custom style. This adversely impacts the overall productivity of the design team, as well as the quality of the design. In spite of this, very little automation has been available to the designers of high performance datapaths. In this paper we present a new "macrodriven " approach to the design of datapath circuits. Our approach, referred to as SMART (Smart Macro Design Advisor), is based on automatic generation of regular datapath components such as muxes, comparators, adders etc., which we refer to as datapath macros. The generated solution is based on designer provided constraints such as delay, load and slope, and is optimized for a designer provided cost metric such as power, area. Results on datapath circuits of a high-performance microprocessor show that this approach is very effective for both designer productivity as well as design quality.

We present a new output encoding problem as follows: Given a specification table, such as a truth table or a finite state machine state table, where some of the outputs are specified in terms of 1's, 0's and don't cares, and others are specified symbolically, and assuming that the minimum number of bits are used to encode the symbolic outputs (#log 2 n# bits for n symbolic outputs), determine a binary code for each symbol of the symbolically specified output column such that the total number of output functions to be implemented after encoding the symbolic outputs and compacting the columns is minimum. There are several applications of this output encoding problem, one of which is to reduce the area overhead while implementing scan or pseudo-random BIST in a circuit with one-hot signals. We develop an exact algorithm to solve the above problem and present experimental data to validate the claim that our encoding strategy helps to reduce the area of a synthesized circuit.